{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-09-06T16:43:10.324317Z",
     "start_time": "2025-09-06T16:43:10.320529Z"
    }
   },
   "source": [
    "from itertools import groupby\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "plt.rcParams['font.family'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "import os\n",
    "# 关键：在导入sklearn相关模块前设置环境变量\n",
    "os.environ[\"OMP_NUM_THREADS\"] = \"2\""
   ],
   "outputs": [],
   "execution_count": 81
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:48.165533Z",
     "start_time": "2025-09-06T16:04:47.861801Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\"\"\"读取附件\"\"\"\n",
    "data = pd.read_excel('附件.xlsx')\n",
    "data"
   ],
   "id": "232345956876ad49",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        序号  孕妇代码  年龄     身高     体重                 末次月经 IVF妊娠  \\\n",
       "0        1  A001  31  160.0  72.00  2023-02-01 00:00:00  自然受孕   \n",
       "1        2  A001  31  160.0  73.00  2023-02-01 00:00:00  自然受孕   \n",
       "2        3  A001  31  160.0  73.00  2023-02-01 00:00:00  自然受孕   \n",
       "3        4  A001  31  160.0  74.00  2023-02-01 00:00:00  自然受孕   \n",
       "4        5  A002  32  149.0  74.00  2023-11-09 00:00:00  自然受孕   \n",
       "...    ...   ...  ..    ...    ...                  ...   ...   \n",
       "1077  1078  A266  30  159.0  83.35           2022-12-29  自然受孕   \n",
       "1078  1079  A267  28  155.0  73.76           2023-02-25  自然受孕   \n",
       "1079  1080  A267  28  155.0  74.06           2023-02-25  自然受孕   \n",
       "1080  1081  A267  28  155.0  74.74           2023-02-25  自然受孕   \n",
       "1081  1082  A267  28  155.0  75.85           2023-02-25  自然受孕   \n",
       "\n",
       "                     检测日期  检测抽血次数   检测孕周  ...    Y染色体浓度    X染色体浓度  \\\n",
       "0                20230429       1  11w+6  ...  0.025936  0.038061   \n",
       "1                20230531       2  15w+6  ...  0.034887  0.059572   \n",
       "2                20230625       3  20w+1  ...  0.066171  0.075995   \n",
       "3                20230716       4  22w+6  ...  0.061192  0.052305   \n",
       "4                20240219       1  13w+6  ...  0.059230  0.059708   \n",
       "...                   ...     ...    ...  ...       ...       ...   \n",
       "1077  2023-05-02 00:00:00       4  17w+5  ...  0.099052  0.056686   \n",
       "1078  2023-05-17 00:00:00       1  11w+4  ...  0.098706  0.023663   \n",
       "1079  2023-05-24 00:00:00       2  12w+4  ...  0.102088  0.080264   \n",
       "1080  2023-05-31 00:00:00       3  13w+4  ...  0.109855  0.074050   \n",
       "1081  2023-06-07 00:00:00       4  14w+4  ...  0.122668  0.212257   \n",
       "\n",
       "      13号染色体的GC含量  18号染色体的GC含量  21号染色体的GC含量  被过滤掉读段数的比例  染色体的非整倍体  怀孕次数  生产次数  \\\n",
       "0        0.377069     0.389803     0.399399    0.027484       NaN     1     0   \n",
       "1        0.371542     0.384771     0.391706    0.019617       NaN     1     0   \n",
       "2        0.377449     0.390582     0.399480    0.022312       NaN     1     0   \n",
       "3        0.375613     0.389251     0.397212    0.023280       NaN     1     0   \n",
       "4        0.380260     0.393618     0.404868    0.024212       NaN     2     1   \n",
       "...           ...          ...          ...         ...       ...   ...   ...   \n",
       "1077     0.376861     0.389914     0.397090    0.017951       T18     1     0   \n",
       "1078     0.377597     0.387901     0.404293    0.022549       T21     1     0   \n",
       "1079     0.379041     0.391748     0.400433    0.021330       NaN     1     0   \n",
       "1080     0.379107     0.388544     0.401030    0.022013       NaN     1     0   \n",
       "1081     0.379130     0.391535     0.401856    0.016906       NaN     1     0   \n",
       "\n",
       "      胎儿是否健康  \n",
       "0          是  \n",
       "1          是  \n",
       "2          是  \n",
       "3          是  \n",
       "4          否  \n",
       "...      ...  \n",
       "1077       是  \n",
       "1078       是  \n",
       "1079       是  \n",
       "1080       是  \n",
       "1081       是  \n",
       "\n",
       "[1082 rows x 31 columns]"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序号</th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>末次月经</th>\n",
       "      <th>IVF妊娠</th>\n",
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       "      <th>18号染色体的GC含量</th>\n",
       "      <th>21号染色体的GC含量</th>\n",
       "      <th>被过滤掉读段数的比例</th>\n",
       "      <th>染色体的非整倍体</th>\n",
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       "      <td>A001</td>\n",
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       "      <td>160.0</td>\n",
       "      <td>72.00</td>\n",
       "      <td>2023-02-01 00:00:00</td>\n",
       "      <td>自然受孕</td>\n",
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       "      <td>自然受孕</td>\n",
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       "      <td>160.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>2023-02-01 00:00:00</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>20230625</td>\n",
       "      <td>3</td>\n",
       "      <td>20w+1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>0.075995</td>\n",
       "      <td>0.377449</td>\n",
       "      <td>0.390582</td>\n",
       "      <td>0.399480</td>\n",
       "      <td>0.022312</td>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
       "      <td>A001</td>\n",
       "      <td>31</td>\n",
       "      <td>160.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>2023-02-01 00:00:00</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>20230716</td>\n",
       "      <td>4</td>\n",
       "      <td>22w+6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061192</td>\n",
       "      <td>0.052305</td>\n",
       "      <td>0.375613</td>\n",
       "      <td>0.389251</td>\n",
       "      <td>0.397212</td>\n",
       "      <td>0.023280</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>A002</td>\n",
       "      <td>32</td>\n",
       "      <td>149.0</td>\n",
       "      <td>74.00</td>\n",
       "      <td>2023-11-09 00:00:00</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>20240219</td>\n",
       "      <td>1</td>\n",
       "      <td>13w+6</td>\n",
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       "      <td>0.393618</td>\n",
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       "      <td>否</td>\n",
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       "      <td>自然受孕</td>\n",
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       "      <td>T18</td>\n",
       "      <td>1</td>\n",
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       "      <td>是</td>\n",
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       "      <th>1078</th>\n",
       "      <td>1079</td>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>73.76</td>\n",
       "      <td>2023-02-25</td>\n",
       "      <td>自然受孕</td>\n",
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       "      <td>2023-02-25</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>2023-05-24 00:00:00</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.080264</td>\n",
       "      <td>0.379041</td>\n",
       "      <td>0.391748</td>\n",
       "      <td>0.400433</td>\n",
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       "      <td>74.74</td>\n",
       "      <td>2023-02-25</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>2023-05-31 00:00:00</td>\n",
       "      <td>3</td>\n",
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       "      <td>0.074050</td>\n",
       "      <td>0.379107</td>\n",
       "      <td>0.388544</td>\n",
       "      <td>0.401030</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>是</td>\n",
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       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>1082</td>\n",
       "      <td>A267</td>\n",
       "      <td>28</td>\n",
       "      <td>155.0</td>\n",
       "      <td>75.85</td>\n",
       "      <td>2023-02-25</td>\n",
       "      <td>自然受孕</td>\n",
       "      <td>2023-06-07 00:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>14w+4</td>\n",
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       "      <td>0.122668</td>\n",
       "      <td>0.212257</td>\n",
       "      <td>0.379130</td>\n",
       "      <td>0.391535</td>\n",
       "      <td>0.401856</td>\n",
       "      <td>0.016906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1082 rows × 31 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 47
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:48.220524Z",
     "start_time": "2025-09-06T16:04:48.212301Z"
    }
   },
   "cell_type": "code",
   "source": [
    "select_cols = ['孕妇代码','孕妇BMI','Y染色体浓度','检测孕周','胎儿是否健康','染色体的非整倍体']\n",
    "origin_df = data[select_cols].copy()\n",
    "origin_df"
   ],
   "id": "1c950be1b2926803",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      孕妇代码      孕妇BMI    Y染色体浓度   检测孕周 胎儿是否健康 染色体的非整倍体\n",
       "0     A001  28.125000  0.025936  11w+6      是      NaN\n",
       "1     A001  28.515625  0.034887  15w+6      是      NaN\n",
       "2     A001  28.515625  0.066171  20w+1      是      NaN\n",
       "3     A001  28.906250  0.061192  22w+6      是      NaN\n",
       "4     A002  33.331832  0.059230  13w+6      否      NaN\n",
       "...    ...        ...       ...    ...    ...      ...\n",
       "1077  A266  32.969881  0.099052  17w+5      是      T18\n",
       "1078  A267  30.703133  0.098706  11w+4      是      T21\n",
       "1079  A267  30.825814  0.102088  12w+4      是      NaN\n",
       "1080  A267  31.107551  0.109855  13w+4      是      NaN\n",
       "1081  A267  31.572341  0.122668  14w+4      是      NaN\n",
       "\n",
       "[1082 rows x 6 columns]"
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       "      <td>0.034887</td>\n",
       "      <td>15w+6</td>\n",
       "      <td>是</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>28.515625</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>20w+1</td>\n",
       "      <td>是</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A001</td>\n",
       "      <td>28.906250</td>\n",
       "      <td>0.061192</td>\n",
       "      <td>22w+6</td>\n",
       "      <td>是</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>33.331832</td>\n",
       "      <td>0.059230</td>\n",
       "      <td>13w+6</td>\n",
       "      <td>否</td>\n",
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       "      <td>是</td>\n",
       "      <td>T18</td>\n",
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       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>A267</td>\n",
       "      <td>30.703133</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>11w+4</td>\n",
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       "      <td>T21</td>\n",
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       "    <tr>\n",
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       "      <td>0.102088</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1082 rows × 6 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 48
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:48.455153Z",
     "start_time": "2025-09-06T16:04:48.440923Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 对数据进行预处理\n",
    "# 定义一个周数转化函数\n",
    "import re\n",
    "def convert_week(week_str):\n",
    "    try:\n",
    "        # 处理空值\n",
    "        if pd.isna(week_str):\n",
    "            return None\n",
    "\n",
    "        # 统一转为小写并清理格式\n",
    "        week_str = str(week_str).lower()\n",
    "        # 移除所有非数字和w的字符\n",
    "        week_str = re.sub(r'[^0-9.w]', '', week_str)\n",
    "        # 处理连续的w\n",
    "        week_str = re.sub(r'w+', 'w', week_str)\n",
    "        parts = week_str.split('w')\n",
    "        # 处理不同格式\n",
    "        if len(parts) == 2 and parts[1] != '':\n",
    "            # 处理类似 \"12w3\" 格式\n",
    "            return float(parts[0]) + float(parts[1])/7.0\n",
    "        elif len(parts) >= 1 and parts[0] != '':\n",
    "            # 处理只有周数的情况，如 \"12\" 或 \"12w\"\n",
    "            return float(parts[0])\n",
    "        else:\n",
    "            # 无法解析的格式\n",
    "            return None\n",
    "    except:\n",
    "        # 处理转换错误\n",
    "        return None\n",
    "# 将周数转化一下\n",
    "origin_df['检测孕周'] = origin_df['检测孕周'].apply(convert_week)\n",
    "origin_df"
   ],
   "id": "3adaafbfe5025997",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      孕妇代码      孕妇BMI    Y染色体浓度       检测孕周 胎儿是否健康 染色体的非整倍体\n",
       "0     A001  28.125000  0.025936  11.857143      是      NaN\n",
       "1     A001  28.515625  0.034887  15.857143      是      NaN\n",
       "2     A001  28.515625  0.066171  20.142857      是      NaN\n",
       "3     A001  28.906250  0.061192  22.857143      是      NaN\n",
       "4     A002  33.331832  0.059230  13.857143      否      NaN\n",
       "...    ...        ...       ...        ...    ...      ...\n",
       "1077  A266  32.969881  0.099052  17.714286      是      T18\n",
       "1078  A267  30.703133  0.098706  11.571429      是      T21\n",
       "1079  A267  30.825814  0.102088  12.571429      是      NaN\n",
       "1080  A267  31.107551  0.109855  13.571429      是      NaN\n",
       "1081  A267  31.572341  0.122668  14.571429      是      NaN\n",
       "\n",
       "[1082 rows x 6 columns]"
      ],
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       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>孕妇BMI</th>\n",
       "      <th>Y染色体浓度</th>\n",
       "      <th>检测孕周</th>\n",
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       "      <td>是</td>\n",
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       "      <td>A001</td>\n",
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       "      <td>20.142857</td>\n",
       "      <td>是</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A001</td>\n",
       "      <td>28.906250</td>\n",
       "      <td>0.061192</td>\n",
       "      <td>22.857143</td>\n",
       "      <td>是</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
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       "      <td>33.331832</td>\n",
       "      <td>0.059230</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>否</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>是</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>A267</td>\n",
       "      <td>31.107551</td>\n",
       "      <td>0.109855</td>\n",
       "      <td>13.571429</td>\n",
       "      <td>是</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>A267</td>\n",
       "      <td>31.572341</td>\n",
       "      <td>0.122668</td>\n",
       "      <td>14.571429</td>\n",
       "      <td>是</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1082 rows × 6 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 49
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:48.855629Z",
     "start_time": "2025-09-06T16:04:48.841640Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 筛选出Y染色体浓度≥0.04的记录\n",
    "qualified_records = origin_df[origin_df['Y染色体浓度'] >= 0.04]\n",
    "# 按孕妇代码分组，找出每组中最小的检测孕周（即最早时间）\n",
    "early_detection = qualified_records.groupby('孕妇代码')['检测孕周'].min().reset_index()\n",
    "# 重命名列名使其更清晰\n",
    "early_detection = early_detection.rename(columns={'检测孕周': '最早达到目标Y染色体浓度的孕周'})\n",
    "# 如果需要将结果与原始数据中的其他信息合并（如BMI、胎儿健康状况）\n",
    "processed_df = pd.merge(early_detection, origin_df[['孕妇代码','检测孕周', '孕妇BMI', \"胎儿是否健康\",'染色体的非整倍体']].drop_duplicates(),\n",
    "                     on='孕妇代码', how='left')\n",
    "processed_df = processed_df[processed_df['检测孕周']>=processed_df['最早达到目标Y染色体浓度的孕周']]\n",
    "processed_df"
   ],
   "id": "b1e29516a194919b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      孕妇代码  最早达到目标Y染色体浓度的孕周       检测孕周      孕妇BMI 胎儿是否健康 染色体的非整倍体\n",
       "2     A001        20.142857  20.142857  28.515625      是      NaN\n",
       "3     A001        20.142857  22.857143  28.906250      是      NaN\n",
       "4     A002        13.857143  13.857143  33.331832      否      NaN\n",
       "5     A002        13.857143  16.714286  34.232692      否      NaN\n",
       "6     A002        13.857143  19.714286  33.782262      否      NaN\n",
       "...    ...              ...        ...        ...    ...      ...\n",
       "1024  A266        13.714286  17.714286  32.969881      是      T18\n",
       "1025  A267        11.571429  11.571429  30.703133      是      T21\n",
       "1026  A267        11.571429  12.571429  30.825814      是      NaN\n",
       "1027  A267        11.571429  13.571429  31.107551      是      NaN\n",
       "1028  A267        11.571429  14.571429  31.572341      是      NaN\n",
       "\n",
       "[961 rows x 6 columns]"
      ],
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       "      <th>检测孕周</th>\n",
       "      <th>孕妇BMI</th>\n",
       "      <th>胎儿是否健康</th>\n",
       "      <th>染色体的非整倍体</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>28.515625</td>\n",
       "      <td>是</td>\n",
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       "      <td>30.825814</td>\n",
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       "      <th>1027</th>\n",
       "      <td>A267</td>\n",
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       "      <td>是</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1028</th>\n",
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       "      <td>是</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>961 rows × 6 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 50
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:48.984650Z",
     "start_time": "2025-09-06T16:04:48.975875Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\"\"\"对婴儿的健康状况进行独热编码\"\"\"\n",
    "def dummy(health_ch):\n",
    "    if health_ch=='是':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "processed_df['胎儿是否健康'] = processed_df['胎儿是否健康'].apply(dummy)\n",
    "processed_df"
   ],
   "id": "60bba6153e229c2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      孕妇代码  最早达到目标Y染色体浓度的孕周       检测孕周      孕妇BMI  胎儿是否健康 染色体的非整倍体\n",
       "2     A001        20.142857  20.142857  28.515625       1      NaN\n",
       "3     A001        20.142857  22.857143  28.906250       1      NaN\n",
       "4     A002        13.857143  13.857143  33.331832       0      NaN\n",
       "5     A002        13.857143  16.714286  34.232692       0      NaN\n",
       "6     A002        13.857143  19.714286  33.782262       0      NaN\n",
       "...    ...              ...        ...        ...     ...      ...\n",
       "1024  A266        13.714286  17.714286  32.969881       1      T18\n",
       "1025  A267        11.571429  11.571429  30.703133       1      T21\n",
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       "1027  A267        11.571429  13.571429  31.107551       1      NaN\n",
       "1028  A267        11.571429  14.571429  31.572341       1      NaN\n",
       "\n",
       "[961 rows x 6 columns]"
      ],
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>最早达到目标Y染色体浓度的孕周</th>\n",
       "      <th>检测孕周</th>\n",
       "      <th>孕妇BMI</th>\n",
       "      <th>胎儿是否健康</th>\n",
       "      <th>染色体的非整倍体</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>28.515625</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>22.857143</td>\n",
       "      <td>28.906250</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>33.331832</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>16.714286</td>\n",
       "      <td>34.232692</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>19.714286</td>\n",
       "      <td>33.782262</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>A266</td>\n",
       "      <td>13.714286</td>\n",
       "      <td>17.714286</td>\n",
       "      <td>32.969881</td>\n",
       "      <td>1</td>\n",
       "      <td>T18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>30.703133</td>\n",
       "      <td>1</td>\n",
       "      <td>T21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>30.825814</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>13.571429</td>\n",
       "      <td>31.107551</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>14.571429</td>\n",
       "      <td>31.572341</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>961 rows × 6 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 51
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:49.304526Z",
     "start_time": "2025-09-06T16:04:49.171042Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 计算风险值\n",
    "grouped = processed_df.groupby('孕妇代码')\n",
    "def get_risk(week):\n",
    "    if week<13:\n",
    "        return 1\n",
    "    elif week<28:\n",
    "        return 2\n",
    "    else:\n",
    "        return 3\n",
    "result_list = []\n",
    "for preg_code in grouped.groups.keys():\n",
    "    # 获取当前孕妇的所有检测记录\n",
    "    preg_data = grouped.get_group(preg_code)\n",
    "    # ---------- 情况1：染色体的非整倍体存在非NaN记录 ----------\n",
    "    non_nan_chrom = preg_data[~preg_data['染色体的非整倍体'].isna()]\n",
    "    if not non_nan_chrom.empty:\n",
    "        first_detection_week = non_nan_chrom.iloc[0]['检测孕周']  # 取首个非NaN记录的检测孕周\n",
    "        result_list.append({\n",
    "            '孕妇代码': preg_code,\n",
    "            '风险值': get_risk(first_detection_week)\n",
    "        })\n",
    "    # ---------- 情况2：染色体的非整倍体全为NaN，但胎儿健康有0 ----------\n",
    "    elif any(preg_data['胎儿是否健康'] == 0):\n",
    "        last_detection_week = preg_data['检测孕周'].max()  # 取最后一次检测孕周\n",
    "        result_list.append({\n",
    "            '孕妇代码': preg_code,\n",
    "            '风险值': get_risk(last_detection_week)\n",
    "        })\n",
    "    # ---------- 其他情况（无符合条件的记录） ----------\n",
    "    else:\n",
    "        first_detection_week = preg_data['检测孕周'].min()  # 取最后一次检测孕周\n",
    "        result_list.append({\n",
    "            '孕妇代码': preg_code,\n",
    "            '风险值': get_risk(first_detection_week),\n",
    "        })\n",
    "result_df = pd.DataFrame(result_list)\n",
    "result_df"
   ],
   "id": "963c60ab9a34d66c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     孕妇代码  风险值\n",
       "0    A001    2\n",
       "1    A002    2\n",
       "2    A003    2\n",
       "3    A004    1\n",
       "4    A005    1\n",
       "..    ...  ...\n",
       "255  A263    1\n",
       "256  A264    1\n",
       "257  A265    1\n",
       "258  A266    2\n",
       "259  A267    1\n",
       "\n",
       "[260 rows x 2 columns]"
      ],
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       "      <th>4</th>\n",
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       "      <td>A267</td>\n",
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     },
     "execution_count": 52,
     "metadata": {},
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    }
   ],
   "execution_count": 52
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:49.597171Z",
     "start_time": "2025-09-06T16:04:49.560654Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 计算每个孕妇的BMI标准差（验证波动幅度）\n",
    "processed_df['bmi_std'] = processed_df.groupby('孕妇代码')['孕妇BMI'].transform('std').fillna(0)\n",
    "\n",
    "# 定义分情况聚合函数（根据BMI波动选择均值/中位数）\n",
    "def robust_agg(group):\n",
    "    if group['bmi_std'].iloc[0] <= 1:\n",
    "        return group['孕妇BMI'].mean()  # 波动小用均值\n",
    "    else:\n",
    "        return group['孕妇BMI'].median()  # 波动大用中位数\n",
    "\n",
    "# 计算每个孕妇的稳健聚合BMI并合并回原表\n",
    "bmi_agg = processed_df.groupby('孕妇代码').apply(robust_agg).reset_index()\n",
    "bmi_agg.columns = ['孕妇代码', '聚合后BMI']  # 重命名列便于合并\n",
    "processed_df = pd.merge(processed_df, bmi_agg, on='孕妇代码', how='left')\n",
    "\n",
    "# 用聚合后BMI替换原始BMI，清理辅助列\n",
    "processed_df['孕妇BMI'] = processed_df['聚合后BMI']\n",
    "processed_df = processed_df.drop(columns=['聚合后BMI', 'bmi_std','染色体的非整倍体'], errors='ignore')\n",
    "processed_df = processed_df.drop(columns=['检测孕周'], errors='ignore').drop_duplicates(keep='first')\n",
    "processed_df = pd.merge(processed_df, result_df, on='孕妇代码', how='left')\n",
    "processed_df"
   ],
   "id": "eee75138ecf65ea9",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\20900\\AppData\\Local\\Temp\\ipykernel_34504\\1428977877.py:12: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  bmi_agg = processed_df.groupby('孕妇代码').apply(robust_agg).reset_index()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "     孕妇代码  最早达到目标Y染色体浓度的孕周      孕妇BMI  胎儿是否健康  风险值\n",
       "0    A001        20.142857  28.710938       1    2\n",
       "1    A002        13.857143  33.962434       0    2\n",
       "2    A003        13.000000  31.226562       1    2\n",
       "3    A004        11.000000  28.721359       1    1\n",
       "4    A005        12.285714  31.077778       1    1\n",
       "..    ...              ...        ...     ...  ...\n",
       "255  A263        12.571429  29.643738       1    1\n",
       "256  A264        11.714286  32.897314       1    1\n",
       "257  A265        11.714286  34.206054       1    1\n",
       "258  A266        13.714286  32.459274       1    2\n",
       "259  A267        11.571429  31.052210       1    1\n",
       "\n",
       "[260 rows x 5 columns]"
      ],
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>孕妇代码</th>\n",
       "      <th>最早达到目标Y染色体浓度的孕周</th>\n",
       "      <th>孕妇BMI</th>\n",
       "      <th>胎儿是否健康</th>\n",
       "      <th>风险值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A001</td>\n",
       "      <td>20.142857</td>\n",
       "      <td>28.710938</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>A002</td>\n",
       "      <td>13.857143</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A003</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A004</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>28.721359</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A005</td>\n",
       "      <td>12.285714</td>\n",
       "      <td>31.077778</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>A263</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>29.643738</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>A264</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>32.897314</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>A265</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>34.206054</td>\n",
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       "      <td>1</td>\n",
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       "      <th>258</th>\n",
       "      <td>A266</td>\n",
       "      <td>13.714286</td>\n",
       "      <td>32.459274</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 5 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 53
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:49.660843Z",
     "start_time": "2025-09-06T16:04:49.654805Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\"\"\"K均值聚类\"\"\"\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "features = processed_df[['孕妇BMI']].copy()\n",
    "# 处理缺失值（根据实际情况选择填充方式）\n",
    "features.fillna(features.mean(), inplace=True) #当然以防万一的，实际上并不存在\n",
    "# 特征标准化（消除量纲影响，K-means对距离敏感）\n",
    "scaler = StandardScaler()\n",
    "scaled_features = scaler.fit_transform(features)"
   ],
   "id": "35c1ddcf1d190b7c",
   "outputs": [],
   "execution_count": 54
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:50.498201Z",
     "start_time": "2025-09-06T16:04:49.814549Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 确定最佳聚类数k\n",
    "# 这里我们使用肘部法则来求解\n",
    "sse = []\n",
    "# 测试聚类\n",
    "k_range = range(2,50)\n",
    "for k in k_range:\n",
    "    md = KMeans(n_clusters=k).fit(scaled_features)\n",
    "    sse.append(md.inertia_)\n",
    "fig = plt.figure(figsize=(16,6))\n",
    "fig.add_subplot(121)\n",
    "plt.plot(k_range, sse, '*-')\n",
    "plt.xlabel('聚类数K')\n",
    "plt.ylabel('误差平方和（SSE）')\n",
    "plt.title('肘部法则确定最佳K值')\n",
    "plt.grid(alpha=0.3)\n",
    "\n",
    "# 利用轮廓系数法来求解\n",
    "sil_scores = []\n",
    "for k in k_range:\n",
    "    labels = KMeans(n_clusters=k).fit_predict(scaled_features)\n",
    "    sil_scores.append(silhouette_score(scaled_features, labels))\n",
    "fig.add_subplot(122)\n",
    "# 绘制轮廓系数图\n",
    "plt.plot(range(2, 50), sil_scores, 'ro-')\n",
    "plt.xlabel('聚类数K')\n",
    "plt.ylabel('轮廓系数')\n",
    "plt.title('轮廓系数确定最佳K值（越接近1越好）')\n",
    "plt.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "5ac527f660813b52",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 55
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:50.622348Z",
     "start_time": "2025-09-06T16:04:50.573896Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from scipy.optimize import minimize_scalar\n",
    "k_range = range(3,6)\n",
    "sil_scores = []\n",
    "risk_scores = []\n",
    "\n",
    "best_tg_per_k = {}\n",
    "clusters_per_k = {}\n",
    "\n",
    "lambda_weight = 10.0  # 未达标风险权重\n",
    "beta_weight = 1.0     # 延迟风险权重\n",
    "\n",
    "def expected_risk(t_candidate):\n",
    "    risk = 0\n",
    "    for t_star_i in group_data:\n",
    "        accuracy_risk = lambda_weight if t_candidate < t_star_i else 0\n",
    "        delay_risk = beta_weight * max(0, t_candidate - 13)\n",
    "        risk += accuracy_risk + delay_risk\n",
    "    return risk / len(group_data)\n",
    "\n",
    "for k in k_range:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
    "    labels = kmeans.fit_predict(scaled_features)\n",
    "    sil_scores.append(silhouette_score(scaled_features, labels))\n",
    "    # 计算每组的最优检测时间与平均风险\n",
    "    total_risk = 0\n",
    "    best_tg_list = []\n",
    "\n",
    "    for label in range(k):\n",
    "        group_data = processed_df[labels == label]['最早达到目标Y染色体浓度的孕周'].values\n",
    "        if len(group_data) == 0:\n",
    "            continue\n",
    "\n",
    "        # 在合理范围搜索最优 t\n",
    "        res = minimize_scalar(expected_risk, bounds=(8, 25), method='bounded')\n",
    "        best_tg = res.x\n",
    "        best_tg_list.append(best_tg)\n",
    "        total_risk += expected_risk(best_tg) * len(group_data)\n",
    "\n",
    "    avg_risk = total_risk / len(processed_df)\n",
    "    risk_scores.append(avg_risk)\n",
    "    best_tg_per_k[k] = best_tg_list\n",
    "    clusters_per_k[k] = labels\n",
    "\n",
    "# 归一化风险得分（反向）\n",
    "risk_norm = (np.max(risk_scores) - np.array(risk_scores))\n",
    "risk_norm = risk_norm / (np.max(risk_norm) + 1e-8)\n",
    "\n",
    "# 综合评分：轮廓系数 + 归一化风险下降\n",
    "combined_score = np.array(sil_scores) + 0.5 * risk_norm\n",
    "optimal_k = k_range[np.argmax(combined_score)]\n",
    "\n",
    "print(f\"最优聚类数 k = {optimal_k}\")"
   ],
   "id": "c5c46f16d6898aab",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最优聚类数 k = 5\n"
     ]
    }
   ],
   "execution_count": 56
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:50.670021Z",
     "start_time": "2025-09-06T16:04:50.657926Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# -------------------------------\n",
    "# Step 3: 生成不重叠 BMI 区间\n",
    "# -------------------------------\n",
    "\n",
    "labels_opt = clusters_per_k[optimal_k]\n",
    "processed_df['group'] = labels_opt\n",
    "\n",
    "# 按组计算 BMI 范围\n",
    "group_summary = processed_df.groupby('group').agg(\n",
    "    bmi_mean=('孕妇BMI', 'mean'),\n",
    "    bmi_min=('孕妇BMI', 'min'),\n",
    "    bmi_max=('孕妇BMI', 'max'),\n",
    "    count=('孕妇BMI', 'size'),\n",
    "    t_star_median=('最早达到目标Y染色体浓度的孕周', 'median')\n",
    ").sort_values('bmi_mean')\n",
    "\n",
    "# 合并为不重叠区间\n",
    "bmi_intervals = []\n",
    "prev_max = 18.5\n",
    "for idx, row in group_summary.iterrows():\n",
    "    low = max(prev_max, row['bmi_min'] - 0.1)\n",
    "    high = row['bmi_max'] + 0.1\n",
    "    bmi_intervals.append((low, high))\n",
    "    prev_max = high\n",
    "\n",
    "# 重新定义为左闭右开 [a, b)\n",
    "interval_strs = []\n",
    "for i, (low, high) in enumerate(bmi_intervals):\n",
    "    if i == len(bmi_intervals) - 1:\n",
    "        interval_str = f\"[{low:.1f}, ∞)\"\n",
    "    else:\n",
    "        interval_str = f\"[{low:.1f}, {high:.1f})\"\n",
    "    interval_strs.append(interval_str)\n",
    "\n",
    "group_summary['bmi_interval'] = interval_strs"
   ],
   "id": "e04d81c40f7d137",
   "outputs": [],
   "execution_count": 57
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:50.710832Z",
     "start_time": "2025-09-06T16:04:50.692804Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# -------------------------------\n",
    "# Step 4: 每组计算最佳检测时间 t_g*\n",
    "# -------------------------------\n",
    "\n",
    "final_recommendations = []\n",
    "\n",
    "for idx, row in group_summary.iterrows():\n",
    "    group_id = idx\n",
    "    interval = row['bmi_interval']\n",
    "    count = row['count']\n",
    "    median_tstar = row['t_star_median']\n",
    "\n",
    "    # 重新优化该组的 t_g*\n",
    "    group_tstars = processed_df[processed_df['group'] == group_id]['最早达到目标Y染色体浓度的孕周'].values\n",
    "\n",
    "    def risk_func(t):\n",
    "        total = 0\n",
    "        for t_star_i in group_tstars:\n",
    "            acc_risk = 10.0 if t < t_star_i else 0\n",
    "            delay_risk = 1.0 * max(0, t - 12)\n",
    "            total += acc_risk + delay_risk\n",
    "        return total / len(group_tstars)\n",
    "\n",
    "    res = minimize_scalar(risk_func, bounds=(8, 25), method='bounded')\n",
    "    best_tg = round(res.x, 1)\n",
    "    expected_risk_val = risk_func(best_tg)\n",
    "\n",
    "    final_recommendations.append({\n",
    "        'BMI区间': interval,\n",
    "        '推荐检测时间(周)': best_tg,\n",
    "        '组内人数': count,\n",
    "        '中位达标时间': round(median_tstar, 1),\n",
    "        '期望风险': round(expected_risk_val, 2)\n",
    "    })\n",
    "\n",
    "result_df = pd.DataFrame(final_recommendations)\n",
    "result_df"
   ],
   "id": "ca025db41cfe68d7",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          BMI区间  推荐检测时间(周)  组内人数  中位达标时间  期望风险\n",
       "0  [20.6, 29.7)       13.9    42    12.7  3.33\n",
       "1  [29.7, 32.0)       13.9    95    12.7  3.37\n",
       "2  [32.0, 34.4)       13.9    70    12.9  3.04\n",
       "3  [34.4, 37.7)       13.7    41    13.3  4.87\n",
       "4     [38.7, ∞)       16.4    12    16.2  8.57"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BMI区间</th>\n",
       "      <th>推荐检测时间(周)</th>\n",
       "      <th>组内人数</th>\n",
       "      <th>中位达标时间</th>\n",
       "      <th>期望风险</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[20.6, 29.7)</td>\n",
       "      <td>13.9</td>\n",
       "      <td>42</td>\n",
       "      <td>12.7</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[29.7, 32.0)</td>\n",
       "      <td>13.9</td>\n",
       "      <td>95</td>\n",
       "      <td>12.7</td>\n",
       "      <td>3.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[32.0, 34.4)</td>\n",
       "      <td>13.9</td>\n",
       "      <td>70</td>\n",
       "      <td>12.9</td>\n",
       "      <td>3.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[34.4, 37.7)</td>\n",
       "      <td>13.7</td>\n",
       "      <td>41</td>\n",
       "      <td>13.3</td>\n",
       "      <td>4.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[38.7, ∞)</td>\n",
       "      <td>16.4</td>\n",
       "      <td>12</td>\n",
       "      <td>16.2</td>\n",
       "      <td>8.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 58
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:51.044711Z",
     "start_time": "2025-09-06T16:04:50.746991Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(k_range, sil_scores, 'o-', label='轮廓系数')\n",
    "plt.plot(k_range, risk_norm, 's-', label='归一化风险下降')\n",
    "plt.plot(k_range, combined_score, 'd-', label='综合评分')\n",
    "plt.axvline(optimal_k, color='r', linestyle='--', label=f'最优k={optimal_k}')\n",
    "plt.xlabel('聚类数 k')\n",
    "plt.ylabel('得分')\n",
    "plt.legend()\n",
    "plt.title('聚类数选择')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "colors = plt.cm.Set1(np.linspace(0, 1, optimal_k))\n",
    "custom_colors = ['#FF5733', '#33FF57', '#3357FF', '#F7B00E', '#FF33F3']  # 示例颜色\n",
    "colors = [custom_colors[label] for label in labels_opt]\n",
    "scatter = plt.scatter(processed_df['孕妇BMI'], processed_df['最早达到目标Y染色体浓度的孕周'], c=colors)\n",
    "plt.xlabel('孕妇BMI')\n",
    "plt.ylabel('估计达标时间 t* (周)')\n",
    "plt.title('BMI vs 最早达标时间（聚类结果）')\n",
    "\n",
    "for idx, row in group_summary.iterrows():\n",
    "    plt.text(row['bmi_mean'], row['t_star_median'], f\"组{idx+1}\",\n",
    "             fontsize=12, color='black', fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "6f9ed462fd5bcded",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 59
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:51.121071Z",
     "start_time": "2025-09-06T16:04:51.111612Z"
    }
   },
   "cell_type": "code",
   "source": "group_summary",
   "id": "5f34c899178f5230",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        bmi_mean    bmi_min    bmi_max  count  t_star_median  bmi_interval\n",
       "group                                                                     \n",
       "3      28.658768  20.703125  29.643738     42      12.714286  [20.6, 29.7)\n",
       "4      30.702615  29.687500  31.862242     95      12.714286  [29.7, 32.0)\n",
       "0      33.087253  31.957044  34.311308     70      12.857143  [32.0, 34.4)\n",
       "1      35.669758  34.414062  37.638738     41      13.285714  [34.4, 37.7)\n",
       "2      41.061482  38.757350  45.714286     12      16.214286     [38.7, ∞)"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bmi_mean</th>\n",
       "      <th>bmi_min</th>\n",
       "      <th>bmi_max</th>\n",
       "      <th>count</th>\n",
       "      <th>t_star_median</th>\n",
       "      <th>bmi_interval</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28.658768</td>\n",
       "      <td>20.703125</td>\n",
       "      <td>29.643738</td>\n",
       "      <td>42</td>\n",
       "      <td>12.714286</td>\n",
       "      <td>[20.6, 29.7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.702615</td>\n",
       "      <td>29.687500</td>\n",
       "      <td>31.862242</td>\n",
       "      <td>95</td>\n",
       "      <td>12.714286</td>\n",
       "      <td>[29.7, 32.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.087253</td>\n",
       "      <td>31.957044</td>\n",
       "      <td>34.311308</td>\n",
       "      <td>70</td>\n",
       "      <td>12.857143</td>\n",
       "      <td>[32.0, 34.4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.669758</td>\n",
       "      <td>34.414062</td>\n",
       "      <td>37.638738</td>\n",
       "      <td>41</td>\n",
       "      <td>13.285714</td>\n",
       "      <td>[34.4, 37.7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.061482</td>\n",
       "      <td>38.757350</td>\n",
       "      <td>45.714286</td>\n",
       "      <td>12</td>\n",
       "      <td>16.214286</td>\n",
       "      <td>[38.7, ∞)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 60
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:51.230418Z",
     "start_time": "2025-09-06T16:04:51.217581Z"
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   "cell_type": "code",
   "source": "processed_df",
   "id": "f257bc9741300007",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     孕妇代码  最早达到目标Y染色体浓度的孕周      孕妇BMI  胎儿是否健康  风险值  group\n",
       "0    A001        20.142857  28.710938       1    2      3\n",
       "1    A002        13.857143  33.962434       0    2      0\n",
       "2    A003        13.000000  31.226562       1    2      4\n",
       "3    A004        11.000000  28.721359       1    1      3\n",
       "4    A005        12.285714  31.077778       1    1      4\n",
       "..    ...              ...        ...     ...  ...    ...\n",
       "255  A263        12.571429  29.643738       1    1      3\n",
       "256  A264        11.714286  32.897314       1    1      0\n",
       "257  A265        11.714286  34.206054       1    1      0\n",
       "258  A266        13.714286  32.459274       1    2      0\n",
       "259  A267        11.571429  31.052210       1    1      4\n",
       "\n",
       "[260 rows x 6 columns]"
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       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>A266</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 6 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 61
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:51.425778Z",
     "start_time": "2025-09-06T16:04:51.414795Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 将推荐时间合并到 processed_df 中\n",
    "group_summary = group_summary.rename(columns={'bmi_interval':'BMI区间'})\n",
    "group_summary = pd.merge(group_summary, result_df[['BMI区间', '推荐检测时间(周)']], on='BMI区间', how='left')\n",
    "group_summary.insert(loc=0, column='group', value=range(5))\n",
    "group_summary"
   ],
   "id": "a160b225ec149f3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   group   bmi_mean    bmi_min    bmi_max  count  t_star_median         BMI区间  \\\n",
       "0      0  28.658768  20.703125  29.643738     42      12.714286  [20.6, 29.7)   \n",
       "1      1  30.702615  29.687500  31.862242     95      12.714286  [29.7, 32.0)   \n",
       "2      2  33.087253  31.957044  34.311308     70      12.857143  [32.0, 34.4)   \n",
       "3      3  35.669758  34.414062  37.638738     41      13.285714  [34.4, 37.7)   \n",
       "4      4  41.061482  38.757350  45.714286     12      16.214286     [38.7, ∞)   \n",
       "\n",
       "   推荐检测时间(周)  \n",
       "0       13.9  \n",
       "1       13.9  \n",
       "2       13.9  \n",
       "3       13.7  \n",
       "4       16.4  "
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       "      <td>70</td>\n",
       "      <td>12.857143</td>\n",
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       "      <td>[38.7, ∞)</td>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "execution_count": 62
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   "metadata": {
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     "end_time": "2025-09-06T16:07:23.113710Z",
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   "cell_type": "code",
   "source": "processed_df",
   "id": "e977802c2868579c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     孕妇代码  最早达到目标Y染色体浓度的孕周      孕妇BMI  胎儿是否健康  风险值  group\n",
       "0    A001        20.142857  28.710938       1    2      3\n",
       "1    A002        13.857143  33.962434       0    2      0\n",
       "2    A003        13.000000  31.226562       1    2      4\n",
       "3    A004        11.000000  28.721359       1    1      3\n",
       "4    A005        12.285714  31.077778       1    1      4\n",
       "..    ...              ...        ...     ...  ...    ...\n",
       "255  A263        12.571429  29.643738       1    1      3\n",
       "256  A264        11.714286  32.897314       1    1      0\n",
       "257  A265        11.714286  34.206054       1    1      0\n",
       "258  A266        13.714286  32.459274       1    2      0\n",
       "259  A267        11.571429  31.052210       1    1      4\n",
       "\n",
       "[260 rows x 6 columns]"
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       "      <th>259</th>\n",
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       "</table>\n",
       "<p>260 rows × 6 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 68
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:08:38.662832Z",
     "start_time": "2025-09-06T16:08:38.645463Z"
    }
   },
   "cell_type": "code",
   "source": [
    "processed_df_ratio = pd.merge(processed_df, group_summary[['group','推荐检测时间(周)']], on='group', how='left')\n",
    "processed_df_ratio"
   ],
   "id": "ba8ea08c6d41ee88",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     孕妇代码  最早达到目标Y染色体浓度的孕周      孕妇BMI  胎儿是否健康  风险值  group  推荐检测时间(周)\n",
       "0    A001        20.142857  28.710938       1    2      3       13.7\n",
       "1    A002        13.857143  33.962434       0    2      0       13.9\n",
       "2    A003        13.000000  31.226562       1    2      4       16.4\n",
       "3    A004        11.000000  28.721359       1    1      3       13.7\n",
       "4    A005        12.285714  31.077778       1    1      4       16.4\n",
       "..    ...              ...        ...     ...  ...    ...        ...\n",
       "255  A263        12.571429  29.643738       1    1      3       13.7\n",
       "256  A264        11.714286  32.897314       1    1      0       13.9\n",
       "257  A265        11.714286  34.206054       1    1      0       13.9\n",
       "258  A266        13.714286  32.459274       1    2      0       13.9\n",
       "259  A267        11.571429  31.052210       1    1      4       16.4\n",
       "\n",
       "[260 rows x 7 columns]"
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A003</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>31.226562</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A004</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>28.721359</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A005</td>\n",
       "      <td>12.285714</td>\n",
       "      <td>31.077778</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>A263</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>29.643738</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>A264</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>32.897314</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>A265</td>\n",
       "      <td>11.714286</td>\n",
       "      <td>34.206054</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>A266</td>\n",
       "      <td>13.714286</td>\n",
       "      <td>32.459274</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>A267</td>\n",
       "      <td>11.571429</td>\n",
       "      <td>31.052210</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 7 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 70
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:15:49.666870Z",
     "start_time": "2025-09-06T16:15:49.653752Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 计算达标率\n",
    "def calculate_compliance_rate(group_df, recommendation_time):\n",
    "    compliant = group_df[group_df['最早达到目标Y染色体浓度的孕周'] <= recommendation_time].shape[0]\n",
    "    total = group_df.shape[0]\n",
    "    return compliant / total if total > 0 else 0.0\n",
    "\n",
    "# 按组计算达标率\n",
    "compliance_rates = []\n",
    "for idx, row in result_df.iterrows():\n",
    "    group_id = row.name  # 组号\n",
    "    group_data = processed_df_ratio[processed_df_ratio['group'] == group_id]\n",
    "    recommendation_time = row['推荐检测时间(周)']\n",
    "\n",
    "    compliance_rate = calculate_compliance_rate(group_data, recommendation_time)\n",
    "    compliance_rates.append({\n",
    "        'BMI区间': row['BMI区间'],\n",
    "        '推荐检测时间(周)': recommendation_time,\n",
    "        '达标率': compliance_rate,\n",
    "        '组内人数': row['组内人数']\n",
    "    })\n",
    "\n",
    "# 生成达标率表\n",
    "compliance_df = pd.DataFrame(compliance_rates)\n",
    "compliance_df"
   ],
   "id": "23a77ce21edaf891",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          BMI区间  推荐检测时间(周)       达标率  组内人数\n",
       "0  [20.6, 29.7)       13.9  0.885714    42\n",
       "1  [29.7, 32.0)       13.9  0.731707    95\n",
       "2  [32.0, 34.4)       13.9  0.333333    70\n",
       "3  [34.4, 37.7)       13.7  0.761905    41\n",
       "4     [38.7, ∞)       16.4  0.926316    12"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BMI区间</th>\n",
       "      <th>推荐检测时间(周)</th>\n",
       "      <th>达标率</th>\n",
       "      <th>组内人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[20.6, 29.7)</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[29.7, 32.0)</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.731707</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[32.0, 34.4)</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[34.4, 37.7)</td>\n",
       "      <td>13.7</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[38.7, ∞)</td>\n",
       "      <td>16.4</td>\n",
       "      <td>0.926316</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 72
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:23:20.672783Z",
     "start_time": "2025-09-06T16:23:20.568606Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 绘制柱状图\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(compliance_df['BMI区间'], compliance_df['达标率'], color='skyblue')\n",
    "plt.xlabel('BMI区间')\n",
    "plt.ylabel('达标率')\n",
    "plt.title('各BMI区间推荐检测时间下的达标率')\n",
    "plt.grid(alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "ae713f2f83d1bf82",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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EiBFKTEzU+++/r2+//VaSlJKSovj4eH3wwQfKyMjQokWLvCP727dv927/0Ucf6ZVXXtHkyZNVu3ZtBQQE5Hr8KlWq+Dz+6KOPvDN8p6SkqG3btnrmmWcUHR0tm83mHWV1uVwKCAjQJ598okOHDnmD1nPPPaewsDDVqFEjx7H8OZU7O8Cef023zWbTv//9bz322GP6+OOPVadOHX9+jJLO/j4yMzN9fh9ut7tQv4/z/fjjj5o1a5b27NmjqVOnFipoDhkyRGfOnNHgwYP19NNP6+qrr5Z0NvyeO0r/6aefKjAwUN26dcvxs0tOTtaOHTvUo0cPxcXF6bbbbsv1+u60tDR98cUXkqSnn35at99+uz766COfyxxuueUWPfnkk95T4LNPVT/XL7/8otjYWI0bN04HDhzQY489phEjRqhDhw66+eabNXfuXH388cfatWuXJkyYUODs5wCAwiN0AwDydOLECW3YsEGffPKJNm/erHbt2mn58uWqX7++9u3bp7lz52rgwIEKDAxUkyZNFBkZ6R0lrVWrll5//XVlZWVp+/btmjZtml566SVNnz7du/969erpvffe0yWXXOIT0C+UzWbTsWPHVKFCBSUnJ2vkyJF69dVX/QpYn3/+eY7ZxM+dxTs3p0+flsfj0dSpU2W327V06VJNmDBBXbt21YQJEyRJDz74oG688cYcE6uda+3atRo9erQWLVqklStX6qmnnlJgYKA3ZLpcLu8p+p988ok3wGZlZWnx4sWqWrWqvv/+e915551at26dkpKSlJ6ers8++0zvv/++PvjgAwUGBmrQoEGqUKGCt7Zsjz76qFwul44dO+ZddvLkSe9tws4PdOfKvvb6/Gu6pbOTtNWpU0dz5szRP//5z3x/lud79dVXfWazl87O0l1U2bPPDx8+XAMGDNCsWbNy/VDh3OvYJalGjRqqXbu2pLOzps+dO1dTpkyR5DvSLflO/Hb+aPrChQsVEhKi6OhodevWTWvXrtXMmTNzfGi1YsUKBQYGKjU1VXfffbdmzpypbt266dSpU6pUqZLP8dxutw4cOKBatWp5eyV7AsS4uDg99dRT+te//qXZs2crOjpaqampevvtt5Wamqpnn31Wb775pkaOHKmuXbtqzpw5uc6cDwAouvzvVQEA+FtKSkpS9+7dFRMToxkzZqhy5cpasGCBRo0apUsvvVRHjhxRSEiIhgwZouXLl2vAgAEKCwvTihUrvKciBwcHq0GDBmrUqJF69eqlZ555RmvXrtVPP/3kPU69evW0Z88excfHq169eheldpfLpWHDhunXX3/V8uXL9fLLL3vvMX7uNeV56dixo3bt2uX9mjx5cp7PjY+P13333aebbrpJKSkpiomJ8Zl1O3vUPDU11Xs/6JSUFCUlJeWYsC0uLk7PPvusgoOD1bhxY40cOVI7duzQDz/8oC1btui5555TcHCwunbtqhUrVviMGMfGxiojI0O33HKLWrZsqapVq2rOnDm6//77FRsbq7p162r79u3as2eP95ZX54+US2dPAe/SpYt+++03SWf74NxJ31wuV54/i7zuo21Zlux2u3r16qU1a9Zo3759ee4jN6NGjfL5fdxzzz2F2j43d955p6ZNm6aNGzdqwIABufbF66+/rq+++kpfffWVhg8f7rPu4Ycf1urVq3Xo0CFJec+af76dO3fqrbfe0iOPPKKIiAjNnDlT3377rcaMGePzvFOnTmnWrFl65JFHJJ29rKNdu3ZKTU3V2LFj1atXL5/nT506Vb1799bJkye9y06fPq3vv/9eixcvVt++fXXq1Cm1atXK+2FYzZo1NWnSJGVlZeno0aN68803NWDAgBy3pQMAXDhGugEAOYSFhalv376qUaOGGjVqpGuuuUYrV67M8/mXXXaZvv32W7ndbjkcDi1fvjzHc7Kv580OKtLZWyC5XC5t3LhRjz76qHbv3n1BdR89elTPPvusfvvtNy1cuFC1a9dW7dq1VbFiRQ0ZMsR7HXNe1/LmNlO2lPcIb926ddWiRQtNnTpV99xzj2JiYhQaGupdv2LFCu/9yiXphx9+8N5TfOnSpbr22mt14sQJTZ06VR9//LF69Oih1atXKzU1VX/++af3NmJJSUkaPXq0XnjhBXXu3Flut1v79u1T9erVdfr0aU2ZMkVPPPGEjh8/LqfTqZkzZ3qvIX7//ffVp08f1atXT6tXr1arVq106tQp76zq53rvvfeUlZWl2rVrq2/fvtqyZYuCgoJUuXJl9e3bV1dffbXi4+NzDd/ZP6Njx47J6XR6J4DL/pl26tTJO7mcvwr7+yiM2267Tenp6Zo4caISExNznGkRFhbmPdW6YsWKPus6dOigChUqaPXq1erXr5/3NnH52bdvn5588kk1aNDAG6ZvuOEGjRkzRiNHjlT16tU1cOBASdKgQYNUpUoV3XvvvZo2bZpsNpumT5+uEydO6Ntvv9XLL7/ss+8+ffpo+fLlmjBhgnf0/bLLLtPy5cu9I+LPPPOMpLP3W1+wYIGaNWumBg0a+FyD36tXL2VmZua4xRkA4MIQugEAubr99tu9fw8ICND06dPVrl27HM+LjY3VzJkzJeU+AVW2vXv3SjobBrKDd1BQkGrUqKF9+/YpMjJSq1atKnK9b731lubMmaOaNWvqgw8+8I7OSmdHr8eMGaN33nlHSUlJuY7ySmfvd71u3bocAezcW3qdy+FwaNiwYXnWdM899+Q4vfyJJ55QVlaW93Ti2NhYrVu3Tv/3f/+n1NRUrV69Wlu2bNFTTz3lDd3ZZsyYodmzZ8vtdsvlcmnp0qWKiorSQw89pAceeEAzZsyQJO9p0Pfee68WL16sH374QS1bttQvv/yitLQ0tWrVStWqVfOpNS0tTStWrNDgwYNVtWpVvfDCC3rhhRd00003aeXKlerWrZsqVKggh8OR6+nY2SPd9913X67LQ0NDc4zQFiQ9PV3jx4/X+PHjfZZ36tSpUPvJS7du3dSuXTufD0r8kX2/+uzZ8NPS0nxu4XauM2fOaO/everZs6eqVq2qWbNm+dyqrUePHtqxY4dmzJihdu3a6ZprrtE999yj+vXr5xg9/9e//qXKlSurS5cuPsvDw8P1zDPP6OWXX9Zdd92lmJgY3X777Tp06FCecwL06NEjx3K32y23263Vq1d7r1cHAFw4QjcAoEDnX296vtzCdnp6unbs2CGXy6WdO3dq7ty5ioiIUNOmTX1Gu+vVq6eTJ0/qsssuu6Aab7jhBiUnJ3uvgz7ffffdp9tuu80nYGWPpGaPnI4ZMybXSdfODSeWZV3QRF4Oh0Mej0fp6ekqV66cHn30UXXu3FnVqlXT559/Lklq27atfvnlF5/tIiMj9eabb6pZs2Y59vn000/neqzIyEg1atRIf/75p4YMGSKHw6E2bdrkmNBOkhYsWKDAwEDv6dsHDhzQJ598otGjR+uNN97Q2rVrNWfOHO+xzr2Xt/S/n+X69esVERGh77//Xi+++GKep53nJvu52T/jlStX5nmf7uznSXnPfu6PvAJ3UlKS93s8depUjvWNGzf2/j05OdkndLvdbv3jH/9QSkqK/vnPf+rll1/WE088oXvvvTfHqLkkvfTSS4qJidE111wjSd5QnZKS4n3Ob7/9pgULFmjIkCHe0H7uiP+9996rRYsWKTExUTExMVq7dq2cTmeOEesjR46oTZs2+uCDD7y3KDuX2+1mlBsALjJCNwDALydPnswRtKSzgSS3030TExN1zz33yG636/LLL1eHDh1yDcTZobso3G63PB6PMjIyVKdOHdWpU0eWZeV57XZwcLD3dlzZf5fkvb46MDAwz2tzf/rpJyUkJOiPP/7wGek9duyY0tLSlJGRkSOcL1++3OdU+++//95nIrlNmzYpLCwsx6jzhfJ4PEpOTlZAQID+9a9/eWcBzw71LVq0UHJysizL0qWXXqr9+/frzTffVL9+/RQUFCTLsjRhwgTVrl1bd999txo2bKh7771XS5Ys0QMPPCBJOa5JDwkJUVRUlPfn16JFC23YsCFHbenp6ZLOhtrzw/K5v4/AwECFhITk+v253W59/fXX2rRpkyQVeGp39i3G/D0t3ePxeO9xni2v0+KTk5N19OhRb5hOS0vTkCFDtGvXLm3YsEEHDhzQ888/r4oVK8rlcqlevXqqUqWKypUr5/MBzCWXXKKVK1eqa9eu3n1n/4wzMzNVs2ZNzZw5Uy1atNDq1av1yy+/6Pfff/ceNygoSGvWrPF+AFbU239djDsIAAB8EboBAAXKzMzUiy++mOf6c0/llnKfcfpc3bt3997m6KmnnvLZ7nzXX3+9du3alet+MjIytHXrVp9RR38MHz5cffr0yRG68/PFF194Tyk+937jc+bM0eLFi2W3232Cmcfj0V133aVRo0bl2FdmZqZcLpcuvfRSn+Uul8vnHtSZmZn66quvlJycLKngIJU9WdvJkyd97rV9vuzR8hYtWmjRokX6448/dOmll3qv8546daq++eYbvfPOO3I4HLrmmmv0yCOPaOXKlerZs6eWLFmid955R06n0/s9REVFKTY2Nt/6JGncuHGKjY1VQEBAjlH77N9HQROTORwOzZ49Wzt37tTtt9/ucw/s3GRfg+7PRHoul0uWZWnhwoW6/vrrJZ29BGDatGk+z5s8ebKOHj2qn376SVWrVvXe/i0gIEB16tTRkCFDFB4ervDwcK1evVpr1qzR5s2b9fnnn+vo0aM6depUjmvjb7nlljxDt91uV+vWrb0/p82bN6tv374+9/r2JzBnn02Q1/XyAICLz2ZdjNlIAAB/aYcOHVLlypULHFEs7SzL8obac6+r9ceJEyfkcDhynB68YsUKLV26VA899JA6d+7sXT5q1Ch5PJ4ct+XKT1xcnEaNGqUdO3Z4lz3yyCP6z3/+o5YtWxZ427MxY8boxIkTmj59ujIzM/O8nlc6G7oyMzO9o/YZGRnev//yyy/au3evunXr5n1+WlqaLMtSuXLltGPHDn388cfq3Lmzrr32Wr+/P+nsadJ79+5VkyZN8ry23h+///67KleuXOQR3by43W6tWrVKN910k7e+48eP6+DBg95gLZ29vvrHH39UdHS0unfvnutp4wXxeDzefrTb7QVexnEx/Prrr+rUqZOWLFmS49Z4AAAzCN0AAJRi5066BgAAyh5CNwAAAAAAhjA9JQAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYMjf7h4kx4+fFvO1lz0BAQ5lZrpLugyg0OhdlEX0LcoqehdlEX1bdtlsUuXKlxT4vL9d6LYsEbrLKH5vKKvoXZRF9C3KKnoXZRF9+9fG6eUAAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCHOki4AAAAAwN+D3W6T3W4r6TJKFbvdJqeTsdDzeTyWPB6rpMu4KAjdAAAAAIyz222qVKk8oRt+8XgsnTiR+pcI3oRuAAAAAMZlj3KvTDyt4+lZJV0OSrHKwU51rXWJ7HYboRsAAAAACuN4epaOprlLugyg2HDxAAAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGOEu6APiy222y220lXUapY7fb5HTyGdH5PB5LHo9V0mUAAAAAyAOhuxSx222qVKk8oRt+83gsnTiRSvAGAAAASilCdymSPcq9MvG0jqdnlXQ5KOUqBzvVtdYlsttthG4AAACglCJ0l0LH07N0NM1d0mUAAAAAAC4QF8kCAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhpRI6N69e7fuvvtuNW/eXJMmTZJlWQVuM3/+fMXExCg6OlpPP/20Tpw4UQyVAgAAAABQdMUeul0ul/r376+oqCh9+OGHSkhIUGxsbL7bbNmyRXFxcVq8eLFWrFihjIwMTZo0qZgqBgAAAACgaIo9dH/99ddKSUnR8OHDVaNGDQ0dOlTLly/Pd5vt27erdevWuuqqq1SzZk116dJFiYmJxVMwAAAAAABFVOyhOz4+Xk2aNFFISIgkKTIyUgkJCfluU7duXX322Wfav3+/jh8/ruXLlysmJqY4ygUAAAAAoMicxX3AlJQURUREeB/bbDbZ7XYlJycrNDQ0121at26tmjVr6pZbbpEkNWrUSI8//niRjh8Q4CjSdsXBbreVdAkog5xOO71Tijmdpfc1B8gLfYuyit4t3Xi/gsL6q7zPLfbQ7XA4FBgY6LMsKChI6enpeYbu1atX6/Dhw1qzZo3CwsI0adIkPffcc5oxY0ahj5+Z6ZYf87aVCKeTyeRReFlZHmVleUq6DOTD5XKXdAlAodG3KKvo3dKL97oorNL+Ptfm5+cBxR66Q0NDtWfPHp9lqampCggIyHObTz75RD179tRVV10lSRoxYoSuu+46nTp1ShUrVjRaLwAAAAAARVXsHzc1atRI27Zt8z4+ePCgXC5XnqPckuR2u3Xs2DHv4z/++MO7HAAAAACA0qrYR7qbN2+u06dPKy4uTt26ddO8efMUExMjh8OhlJQUBQUF5Rj1jo6O1oIFC1S1alUFBwfrnXfeUdOmTVWpUqXiLh8AAAAAAL8Ve+h2Op0aP368hg0bpsmTJ8vtdmvx4sWSpK5du+qll15Shw4dfLZ5+OGH9ccff2j27Nk6ceKEmjZtqgkTJhR36QAAAAAAFIrNskpmWrGjR49qx44dio6OVlhYWLEd99ix06V6IrVKlcprQfwJHU3j1HnkLzzEoUfqV9KJE6mleoKJv7vAQAeT+qDMoW9RVtG7pRvvdeGvsvI+12aTqlS5pMDnFftId7bw8HCFh4eX1OEBAAAAADCOefsBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAxxlnQBAAAAKBy73Sa73VbSZZQ6drtNTidjSufzeCx5PFZJlwH8bRG6AQAAyhC73aZKlcoTuuE3j8fSiROpBG+ghBC6AQAAypDsUe6Viad1PD2rpMtBKVc52KmutS6R3W4jdAMlhNANAABQBh1Pz9LRNHdJlwEAKAAXvQAAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEGdJFwDgr8Fut8lut5V0GaWO3W6T08nnm+fzeCx5PFZJlwEAAGAcoRvABbPbbapUqTyhG37zeCydOJFK8AYAAH95hG4AFyx7lHtl4mkdT88q6XJQylUOdqprrUtkt9sI3QAA4C+P0A3gojmenqWjae6SLgMAAAAoNbjQEAAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCkREL37t27dffdd6t58+aaNGmSLMvye9shQ4Zo/PjxBqsDAAAAAODiKPbQ7XK51L9/f0VFRenDDz9UQkKCYmNj/dr2m2++0ebNmzVo0CDDVQIAAAAAcOGKPXR//fXXSklJ0fDhw1WjRg0NHTpUy5cvL3C79PR0jRs3TsOGDVPFihWLoVIAAAAAAC5MsYfu+Ph4NWnSRCEhIZKkyMhIJSQkFLjd7NmzlZ6eLqfTqU2bNhXqlHQAAAAAAEqCs7gPmJKSooiICO9jm80mu92u5ORkhYaG5rrN4cOHtWDBAjVu3FiHDx/WwoULVa1aNc2cOVM2m61Qxw8IcFxQ/SbZ7YX7XgBJcjrtJd47JX18lE2loXeRO6ez9P5fCV5zUTSl4TW3pI+Psqc09O3FUOyh2+FwKDAw0GdZUFCQ0tPT8wzdsbGxqlKlihYsWKDAwEA99NBDat++vf7973+rZcuWhTp+ZqZbpXWQ3OlkMnkUXlaWR1lZnhKtgd5FUZSG3kXeXC53SZeAPPCai6IoDa+59C4KqzT0bX78Hf8t9s4PDQ1VUlKSz7LU1FQFBATkuc3Ro0d1ww03eMN6hQoVVLNmTR08eNBorQAAAAAAXIhiD92NGjXStm3bvI8PHjwol8uV5yi3JFWtWlUZGRnexx6PR0eOHFH16tWN1goAAAAAwIUo9tDdvHlznT59WnFxcZKkefPmKSYmRg6HQykpKcrMzMyxTadOnfTFF19o3bp1OnLkiP75z3/K5XIpOjq6mKsHAAAAAMB/xR66nU6nxo8frzFjxigmJkbr1q3TsGHDJEldu3bVV199lWObq666Sq+99prmzJmjW2+9VV999ZVmz56tChUqFHf5AAAAAAD4rdgnUpOkDh066NNPP9WOHTsUHR2tsLAwSdKGDRvy3KZt27Zq27ZtMVUIAAAAAMCFK5HQLUnh4eEKDw8vqcMDAAAAAGAc8/YDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgiN+he/PmzXK73fJ4PN5lBw8elMvlksvl0l133WWkQAAAAAAAyiq/Q/fIkSO1e/dude/eXampqbIsS0OHDtW6devkdDrldrtN1gkAAAAAQJnj9PeJISEhql+/vho2bKinn35aXbt2VdWqVXXHHXdIkhwOh7EiAQAAAAAoi/we6Q4KCpLNZtP48ePVu3dvdezYUbfddptmzpwpSbIsy1iRAAAAAACURYWeSG3ixImqW7euNm7cqOeff14VK1Y0URcAAAAAAGWeX6F7165dOn36tCQpNDRUAwYM0BVXXKF+/fqpevXqOnDggLKysnTgwAHt3r3baMEAAAAAAJQVBV7TnZSUpLvvvlvly5fXm2++qR49esjlcmnMmDHq0aOHnnvuOTmdTp05c0bdu3eXy+XStm3biqN2AAAAAABKtQJHusPCwrRx40ZdccUVSkxM1IMPPqiDBw+qVatWevPNNzV48GBt2bJFdevW1ZYtWwjcAAAAAAD8f36dXl6xYkXZ7XZNmDBBU6ZM0bfffquIiAi1a9dOP//8s+ESAQAAAAAom/y+ZVj27OS1atXS2LFj1ahRI6WlpWnIkCGSJJvNZqZCAAAAAADKKL9nLz9z5ozS0tL0+OOPa/v27apUqZIWLFigX3/9VRK3DAMAAAAA4Hx+h+5KlSopPT1dN9xwg1544QWVL19ejz32mCZMmKDMzExlZGSYrBMAAAAAgDLH79PL33vvPUnS4MGDvcu6d++url27ymazqUuXLhe9OAAAAAAAyjK/Q/e5XC6XAgMD5XQ65XSe3cWdd955UQsDAAAAAKCs8+v0crfbrbi4OEmSx+NRdHS0d93x48c1bNgwderUSYcPHzZSJAAAAAAAZZHf13SPGjXq7AZ2u0JCQiRJ//73v9WpUyclJSVp4cKFql69upkqAQAAAAAog/w6vdzhcHiDtiTvKeX16tXT2LFj1blzZzPVAQAAAABQhvl9TbfD4fD+PSMjQ8OGDfM+Xr9+vSQpKChIPXv2VKNGjS5iiQAAAAAAlE1+h+5z78PtcDjUpk2bHM/55ptvNHXqVL3zzjsXpzoAAAAAAMqwIoVup9Oprl276rffftO3336re+65R0FBQQoNDVVsbKyRQgEAAAAAKGv8mkgtIyNDqamp3seZmZmSpMOHD+v9999XmzZtNH36dDVq1EjTp083UykAAAAAAGWMX6E7MDBQq1ev1smTJ/Xll1+qWbNmcrvd+v3337Vw4ULNnz9fO3bs0MqVK03XCwAAAABAmeHX6eU2m001atTQb7/9pvXr12vKlCnq0aOHPB6P6tSpo8aNG2v+/PnyeDym6wUAAAAAoMzwK3QPHjxYISEhOnPmjPbs2aNXXnlFKSkpio6O1pIlS7RkyRJJksfjUWZmpqZNm2a0aAAAAAAAygK/QvdNN92kkJAQnTx5Ujt37tQvv/yi/fv3y2azqVevXqpSpYqks9d6u1wuowUDAAAAAFBW+BW6e/ToIUlKTExUfHy8Xn75ZSUmJmrRokWaNWuWBg8erJ49exotFAAAAACAssavidSyhYWFqUuXLpKkWrVqadSoUXr77beVkJAgy7KUlJSkP//800ihAAAAAACUNYUK3U6nUzNmzPBZFhUVpTvvvFOzZ8/WkSNH1Lp164taIAAAAAAAZVWhQrfNZtPOnTt9lm3fvl19+/ZVSkqKLr30UlmW5b2PNwAAAAAAf2d+XdOdzeFwyGazeR//8ssveuyxxzRo0CD17t1bSUlJstlssizrohcKAAAAAEBZU2DoTk1N1b59+xQaGipJPqH73Xff1Ysvvqju3bv7rHM4HCZqBQAAAACgTCkwdP/0008aOHCgAgICZLPZdObMGd1www1q2LChunXrpk6dOnmfm5WVpYCAAEI3AAAAAADy45ruli1batu2bdq6dau+/fZbhYSEaPHixbr55ps1d+5cde/eXfv27ZMknT59WpUrVzZeNAAAAAAAZUGhJlLLVqdOHfXs2VMff/yxOnfurPvvv1/x8fE6dOiQatasebFrBAAAAACgTCrURGpZWVk+M5PbbDY98cQTCg0N1bBhw9S6dWs1a9bsohcJAAAAAEBZVKjQ7Xa7NW3atBzL77//fjVs2FDPPvus3nvvvYtWHAAAAAAAZZlfofvMmTN6/vnnZVmWZs2aJUkaNmyYAgMDvc/ZuXOn+vbtq7CwMDOVAgAAAABQxhR4Tbfb7VavXr2UlpamsWPHSpIsy9L69etVt25d71fDhg319ddfy+Vyma4ZAAAAAIAyocCRbofDodGjR6tJkyay289mdJfLJbvdrr59+/o895lnntHEiRM1ZswYM9UCAAAAAFCG+HV6edOmTX0eBwYGasmSJTmeN3HiRGVkZFycygAAAAAAKOMKNZFaNpvNpsjIyBzLy5cvr/Lly19wUQAAAAAA/BUU6T7dAAAAAACgYIRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwpkdC9e/du3X333WrevLkmTZoky7L83jYzM1N33HGHvvvuO4MVAgAAAABw4Yo9dLtcLvXv319RUVH68MMPlZCQoNjYWL+3nz9/vnbv3m2wQgAAAAAALo5iD91ff/21UlJSNHz4cNWoUUNDhw7V8uXL/do2MTFRb7/9tq644grDVQIAAAAAcOGKPXTHx8erSZMmCgkJkSRFRkYqISHBr21Hjx6tfv36EboBAAAAAGWCs7gPmJKSooiICO9jm80mu92u5ORkhYaG5rndhx9+qJSUFPXt21fffPNNkY8fEOAo8ram2e22ki4BZZDTaS/x3inp46NsKg29i9w5naX3/0rwmouiKQ2vuSV9fJQ9paFvL4ZiD90Oh0OBgYE+y4KCgpSenp5n6E5KStK0adM0f/58OZ0XVnJmpluFmLetWDmdTCaPwsvK8igry1OiNdC7KIrS0LvIm8vlLukSkAdec1EUpeE1l95FYZWGvs2Pzc/PA4q980NDQ5WUlOSzLDU1VQEBAXluM2HCBN1zzz1q0KCB6fIAAAAAALhoij10N2rUSNu2bfM+PnjwoFwuV76nlq9atUqLFi1Ss2bN1KxZM/3www/q37+/5s2bVxwlAwAAAABQJMV+ennz5s11+vRpxcXFqVu3bpo3b55iYmLkcDiUkpKioKCgHKPe69ev93k8dOhQPfzww2rVqlVxlg4AAAAAQKEU+0i30+nU+PHjNWbMGMXExGjdunUaNmyYJKlr16766quvcmwTERHh8xUUFKQqVaqoYsWKxV0+AAAAAAB+K/aRbknq0KGDPv30U+3YsUPR0dEKCwuTJG3YsMGv7RctWmSyPAAAAAAALooSCd2SFB4ervDw8JI6PAAAAAAAxjFvPwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwxFnSBQAAUFLsdpvsdltJl1Hq2O02OZ18Ln8+j8eSx2OVdBkAgDKG0A0A+Fuy222qVKk8oRt+83gsnTiRSvAGABQKoRsA8LeUPcq9MvG0jqdnlXQ5KOUqBzvVtdYlsttthG4AQKEQugEAf2vH07N0NM1d0mUAAIC/KC7YAgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYEiJhO7du3fr7rvvVvPmzTVp0iRZllXgNkuXLlXLli0VFRWlvn376o8//iiGSgEAAAAAKLpiD90ul0v9+/dXVFSUPvzwQyUkJCg2NjbfbbZu3arp06dr8uTJWr9+vTIyMjRp0qRiqhgAAAAAgKIp9tD99ddfKyUlRcOHD1eNGjU0dOhQLV++PN9t9u3bp7FjxyomJkZVq1ZV9+7d9csvvxRTxQAAAAAAFI2zuA8YHx+vJk2aKCQkRJIUGRmphISEfLfp0aOHz+N9+/apZs2axmoEAAAAAOBiKPbQnZKSooiICO9jm80mu92u5ORkhYaGFrj9iRMntHTpUk2ZMqVIxw8IcBRpu+Jgt9tKugSUQU6nvcR7p6SPj7KppHuXvkVRlHTfSvQuiobeRVlUGvr2Yij20O1wOBQYGOizLCgoSOnp6X6F7nHjxqlp06Zq27ZtkY6fmemWH/O2lQink8nkUXhZWR5lZXlKtAZ6F0VR0r1L36IoSrpvJXoXRUPvoiwqDX2bH5ufnwcUe+gODQ3Vnj17fJalpqYqICCgwG2XL1+urVu3Ki4uzlB1AAAAAABcPMX+cVOjRo20bds27+ODBw/K5XIVOMq9fft2vfLKK5o2bZqqVKliukwAAAAAAC5YsYfu5s2b6/Tp097R6nnz5ikmJkYOh0MpKSnKzMzMsc2xY8fUv39/9evXT1FRUUpNTVVqamoxVw4AAAAAQOEUe+h2Op0aP368xowZo5iYGK1bt07Dhg2TJHXt2lVfffVVjm1WrVql48eP6/XXX1d0dLT3CwAAAACA0qzYr+mWpA4dOujTTz/Vjh07FB0drbCwMEnShg0bcn1+nz591KdPn2KsEAAAAACAC1cioVuSwsPDFR4eXlKHBwAAAADAOObtBwAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAISUSunfv3q27775bzZs316RJk2RZVoHbfP/99+rUqZOuv/56LViwoBiqBAAAAADgwhR76Ha5XOrfv7+ioqL04YcfKiEhQbGxsfluk5SUpCeffFJdunTR0qVL9fHHH2vz5s3FVDEAAAAAAEVT7KH766+/VkpKioYPH64aNWpo6NChWr58eb7brFy5UpdddpkGDhyoWrVqacCAAQVuAwAAAABASXMW9wHj4+PVpEkThYSESJIiIyOVkJCQ7za7du3SDTfcIJvNJklq3Lixpk2bVqTj//9dlGrhIU4FcLU9ChAW9L9/vqWlr+ld+KO09S59C3+Utr6V6F34h95FWVQa+zY3/tZW7KE7JSVFERER3sc2m012u13JyckKDQ3Nc5urr77a+7hChQo6evRokY5fufIlRdquOHWuWfprROlRqVL5ki7Bi95FYZSW3qVvURilpW8leheFQ++iLCpNfXshiv0zJofDocDAQJ9lQUFBSk9P93ubgp4PAAAAAEBpUOyhOzQ0VElJST7LUlNTFRAQ4Pc2BT0fAAAAAIDSoNhDd6NGjbRt2zbv44MHD8rlcuV5anlu2/z3v/9VeHi40ToBAAAAALhQxR66mzdvrtOnTysuLk6SNG/ePMXExMjhcCglJUWZmZk5tmnfvr1++OEHbd68WVlZWXr77bfVsmXLYq4cAAAAAIDCsVmWZRX3QT///HMNGzZM5cuXl9vt1uLFi1W3bl21b99eL730kjp06JBjm3fffVcTJ05UhQoVVK5cOS1btkxVqlQp7tIBAAAAAPBbiYRuSTp69Kh27Nih6OhohYWF+bXNb7/9poSEBLVo0UIVKlQwXCEAAAAAABemxEI3AAAAAAB/ddyWHgAAAAAAQwjdkCRt3bpVzZo1834dPny4pEsqlbKysjR37lxxgkjpQe/6h94tfehd/9C7pQt96x/6tmz5u/U1/Vn8CN2QJLndbsXExCguLk5xcXG6/PLLS7qkUsnpdOrgwYOaNWtWSZeC/++v0LsJCQnavn27srKyjB2D3i198uvdo0eP6scff1RKSkoJVlgwevfvh771D31btvwV+row6M/iR+iGV7ly5RQREaGIiAg5nU5J0tKlS9WyZUtFRUWpb9+++uOPP7zP//7779WpUyddf/31WrBgQaGOZVmW7r//fr399tsXXLfb7daYMWMUHR2ta6+9VuPGjfP+R5qSkqIRI0bopptuUvv27bVo0SK/9tm+fXtFRkb6fLVv316SNGbMGH366afat2/fBdeOi6MwvWtZlt58803deuutuv766zVu3DidOXOmwGO8+OKLOXoiMjJSBw8eLHLdHo9HAwcOVN++fTV06FB16dJFx44dk5R/XxfktddeU/PmzXXHHXcoPj7eu5zeLX1y6923335bt99+u8aMGaM2bdro+++/9z4/v9fk/Kxdu1bt2rVTy5YttWrVqguuO7/ePdeBAwfUpEmTQu//xx9/1M033+x9TO+WLoXt22yZmZm644479N133xXqeKdOndJNN910Qa+3Uv59++CDD+b6Gl8Q3i/8dRS2r+fPn6+YmBhFR0fr6aef1okTJwo8hon3EkVFfxYzC7Asa/PmzdYLL7zgs2zLli3WjTfeaP373/+2fv/9d+uBBx6whg4dalmWZR0/ftyKjo62ZsyYYe3bt8+66667rE2bNvl9vPfee8/q3Lmz5XK5Lrj22bNnWz169LD27Nlj7dy507rhhhuspUuXWpZlWcOGDbMefPBB68CBA9bmzZutFi1aWMuWLStwn6dPn7aSk5O9X9OmTbOGDx/uXf/ZZ5/l+HmhZBS2d5ctW2a1atXK2rZtm5WQkGB1797devbZZws8zpkzZ3x64quvvrJuvfVWKysrq8i1f/DBB1avXr2szMxMy+12W3369LGmT59uWVb+fZ2fJUuWWC1atLC2bNliff7551anTp2sjIwM73p6t/TIrXd//fVXKyYmxjp69KhlWZY1c+ZMq1evXpZl5d/X+dm1a5cVFRVlLVu2zIqPj7duueUWKyEh4YJqz693z/XII49Y9erVK9S+XS6X1aVLF6tdu3Y+y+nd0qGwfXuu2bNnW/Xq1bM2b95cqGOOGDHCqlevnnXgwIGiF27l37cpKSk+r/FLliyxHnrooQL3yfuFv4bC9vX3339vdenSxUpISLASExOtfv36+fV7NvFe4kLQn8WHkW7kad++fRo7dqxiYmJUtWpVde/eXb/88oskaeXKlbrssss0cOBA1apVSwMGDNDy5cv92u+xY8c0bdo0jRw5UgEBARdc5/HjxzV16lTVqVNHDRo0UOvWrfXLL7/I5XJp7dq1eumllxQREaHrr79ed999t9avX1/gPitUqKCKFSuqYsWKstvt+vDDDzVw4EDv+jZt2mjr1q1GT01D0eXXu3FxcXr00UfVuHFjXXXVVXrmmWf86omQkBBvT1SsWFH/+te/9PTTT8vhcBS5zssvv1wjRoyQ0+mU3W5XvXr1dOrUKUl593VBlixZokcffVTNmjXTzTffrNq1a2vr1q3e9fRu6eZ2uzV+/HjvqY3169dXcnKypPz7Oj8ffPCBrr/+evXo0UORkZHq1auXPvroowuqM7/ezRYXF6fff/+90PueN2+ePB5PjuX0bumVX99mS0xM1Ntvv60rrriiUPvesmWLNmzYoEsvvfSC68yvb8uXL+99fa9QoYIWLlyowYMHF7hP3i/8deXX19u3b1fr1q111VVXqWbNmurSpYsSExML3KeJ9xLS2dsqDx8+XH369NGkSZOUlJQk6ewZn/PmzdMLL7ygadOm6ejRoz7b0Z/Fh9CNPPXo0UO33nqr9/G+fftUs2ZNSdKuXbt0ww03yGazSZIaN26snTt3+rXfiRMnqlq1ajpy5Ih+/vnnC65z5MiRqlGjhk+dNWrU0OnTp5WZmalq1ap51zkcjkK/sL3//vtq06aNzxuFgIAARUREFOkNJczLr3dPnDih6tWre9fZ7fZC98T27dt16NAhde7c+YLqbN26tRo0aCDp7Gm4a9asUYcOHSTl3df5sSxLu3fv1g033OBd1rhxY/3nP//xPqZ3S7c6dep4T01NTU3V4sWLvb2cX1/nJz4+PkdP+Pt6nZf8elc6++9sypQpmjBhQqH2u2/fPi1cuFCjRo3KsY7eLb3y69tso0ePVr9+/QoVul0ul0aPHq2RI0eqfPnyF1xnQX2bbd26dbr88svVtGnTQu2f9wt/Lfn1dd26dfXZZ59p//79On78uJYvX66YmJhC7f9ivZdIT0/XgAED1LNnT82ePVtNmzbV448/rm+//VZ9+vRRgwYNNH78eN14443q16+fUlNTvdvSn8WH0A2/nDhxQkuXLtUDDzwg6ewnZxEREd71FSpUyPHpWW5+/vlnrVq1StWrV9ehQ4f0/PPPF/pNWX42bdqkPXv26K677lKlSpUUHh6uzz//XJJ05swZrV27VjfddJPf+/N4PFqyZIl69eqVY11YWJiOHz9+0WqHGef3boMGDbw9IUmxsbGF6glJWrx4sXr27Cm7/eK8hE6fPl233Xab2rZt6xOOsp3b1/lJTU2Vx+Mp8N8mvVv6ffXVV2rZsqWOHTum/v3751h/fl/nJzU1tUiv1/7Iq3dfffVVderUSdHR0YXa3+jRo/Xkk0/qyiuvzHU9vVu65dW3H374oVJSUtS3b99C7W/u3LmqVavWBYeS8xX0mrt48eJc/9/PD+8X/rpy6+vWrVurZs2auuWWWxQTE6O0tDQ9/vjjhdrvxXovsWPHDl177bVq3LixypUrp1tvvVUvvviiHn30UT377LNq1aqVAgMDdeONN6pFixY55lSgP4sHoRt+GTdunJo2baq2bdtKOjtiHBgY6F0fFBSk9PT0AvezdOlSXXvttZozZ46eeuopvfPOO1q0aNFFmcQhNTVVo0aN0sCBA1W5cmXZ7Xa9/PLLmjp1qvr376+OHTsqOTlZXbt29Xuf33zzjcLCwnTNNdfkerxy5cpdcN0w6/zeHTJkiLZv364HHnhAXbt21erVq9W7d2+/93fy5EmtX7++wABcGP369dOUKVO0bt06nw8EpJx9nZ/siV/O/bcZGBiY498mvVv63XTTTXrzzTflcDg0ZcqUHOvP7+v8FPX12h+59e6mTZu0ZcsWDR06tFD7+uCDD5SRkaGHHnooz+fQu6Vbbn2blJSkadOmacKECd7XKH8kJCTo/fff19ixYy96nfm95u7Zs0f79+/3jnD6i/cLf1259fXq1at1+PBhrVmzRt99953q1q2r5557zu99Xsz3EhEREfrPf/7jnV09NTVVc+bM0eOPP65Zs2Z5T4l3u93673//q9q1a/tsT38WD/9f/fC3tXz5cm3dulVxcXHeZaGhod7rRaSz/2D9uT776NGjatWqlfe09GrVqiksLEwHDx7M8SJQWOPGjdMVV1zh80l669at9eWXX2rfvn167LHH1LdvX1WoUMHvfX788cfq1KlTrusOHTrkc+o6Sp/cejciIkKrV6/Wr7/+qilTpqhKlSpq1qyZ3/v87LPP1Lx5c4WGhl60OsuVK6fOnTtr3759io2N9TndMbe+zktwcLCCg4OVlJTk7fPc/m3Su6Wf0+lUs2bNNHLkSPXv318vvfSSd11ufZ2for5e++P83m3VqpXGjBmjf/zjH4V6E3f8+HFNnz5dCxYsyHfUh94t3XLr2wkTJuiee+7xntbtD8uyNGrUKA0ePFjh4eEXvc78XnNXrVqlW2+9tdCXHfF+4a8rt77+5JNP1LNnT1111VWSpBEjRui6667TqVOnVLFixQL3eTHfS1SrVk3PPPOMhg4dqqpVq+rkyZN64okn1KJFC23dulUvvviiypUrpxMnTqhHjx453m/Tn8WD0I18bd++Xa+88ormzp2rKlWqeJc3atRIn3zyiffxf//7X7/+Y6xataoyMjK8j1NTU5WcnOxzjW1RLFq0SJs3b9aKFStyvGELCgpSYmKibDabHn74Yb/36XK5tGHDBg0aNCjHuv379ys0NFSXXHLJBdUNc/LqXUmy2WyqUKGCNm7cqCVLlhRqv2vWrLlopzpOmjRJbdq08Z7e6HQ6fd7o5dfXeWnYsKF+/vln7/Xf8fHxqlWrlnc9vVu6ffzxx/rzzz+9H7KcPw9Ffn2dl0aNGmnbtm3q0aOHJP9fr/OTV+9u27ZN+/fvzzEBVbNmzTR37tw8P+D6+uuvlZSUpJ49e0o6e6rumTNn1KxZM61cuVLVq1end0ux/Pp21apVKl++vN59911JZy/16t+/v5588sk8T8c9fPiwfvjhB+3evVuTJ0+WdPaytq5du2rcuHG64447ilRnQa+50tnb6xV2dJ33C39N+fW12+32uU1i9u0b3W63X/u+mO8lpLO3rsvt7IxmzZrlO7BAfxYfQjfylH3tSr9+/RQVFeWdeKF8+fJq3769/vGPf2jz5s1q1qyZ3n77bbVs2dK77alTp1S+fPkc/5ndcccdGjx4sGJiYlSjRg1Nnz5dNWvWVO3ateXxeJSSkqJLLrnEOxLuj02bNmny5MmaN2+egoODlZqaKofDoeDgYEln37zNnDlTgwYNUkhIiHe7rKwspaWl5flC88MPPyg0NDTXawtnz56t+++/3+8aUbzy691ss2fP1m233aaoqCifbfPqXensZCVbtmzRuHHjfJYXtXerVaumMWPGaOrUqXI4HFqyZIn39LT8+jq/3u3YsaPmz5+vm2++WceOHdO6det87k9P75ZuV111lUaPHq0rr7xSDRo00IwZM9SxY0dJBfd1Xr3bsWNH9ezZU3369FH16tW1ePFi3X777ZIufu82adIkx+m6N998s+Li4nTZZZfl2bu33nqrmjdv7n185MgRDR06VO+995535mB6t/TKr2/PvzvE0KFD9fDDD6tVq1aScu/b8PDwHNs98MADmjZtmho0aGDkNVc6O7naoUOHcsxFwPuFv6f8+jo6OloLFixQ1apVFRwcrHfeeUdNmzZVpUqVJBXve4kLQX8Wo5K+ZxlKh9zuT7hgwQKrXr16Ob6yLV682IqKirKuv/56q127dtaff/7pXVevXj1r586duR4rNjbW6tixo9WoUSOrR48e1t69ey3LsqwDBw5Y9erVs5KTkwtV+5NPPpmjxt69e3vXf/jhh9Ydd9xhud3uHN/zddddl+d+X3vtNWvw4ME5lq9fv97q2bOn5fF4ClUnzChK7yYmJlrR0dHW4cOHc+wvv97duHGjFRMTk2N5UXvX7XZbr776qtWiRQurZcuW1oIFC7zr8uvr/Ho3IyPD6t27txUdHW01bNjQGj16tHcdvVu65Na7lmVZcXFxVrt27axmzZpZI0aMsM6cOWNZVsF9nV/vTp061YqKirKaNWtmdevWzUpLS7Msy0zvnu/cGgt63c124MABn/t007ulR2H79ny9e/f2uU93fn17rnbt2nnv022qb5ctW2b16NEjx3a8X/jrK2xfp6enW+PHj7datmxpRUVFWb1797Z+++0373bF+V6iqOjP4kXohmVZZ19soqKirOuuu8667rrrrEOHDvm1XWJiorV+/Xrr9OnTF6WOF198sVT/48/MzLTuvfde68iRIyVdCv6/ovbuxVaaejcrK8vatGmT9cMPP3iX0bulT3H37n//+1/ryy+/tDIyMnyWl6bezQ29W7rwmusf+rZs+bv1Nf1Z/GyWZVklPdqOkpeRkaE///zT+7hq1aqFmmX0YsjMzNRPP/2kFi1aFOtxC8vtdhd6ghWYQ+/6j94tXehd/9G7pQd96z/6tuz4O/Y1/Vm8CN0AAAAAABjCfboBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwCACzZ06FBt3bpV0tlJiTIyMsS0MQAASMU7LR8AACi12rdvr+Tk5AJn7fV4PHrqqaf08MMPe5fVqlVLzz33nD766CNNmTJFn3zyiQICAny2O3nypFavXq2rr77aSP0AAJRGhG4AAMqg2NhYDR8+XJIUEBCgyMhIjRkzRu+9955WrFihjz/+WPXq1dPkyZP11ltvaeHChbr++uu9277zzjv66KOPfPYZHBysiRMnep9XGP369dO2bdt08uRJjR8/XuPHj8/xnKZNm+YI4gAA/NVxejkAAGVUvXr1tGXLFn399ddq27atBg0a5F23d+9enz/9Ybf/723Bpk2bdPz4cZ/1y5Yt0/Dhw5WWluZddvDgQbVv316S9NZbb6lGjRp+HwMAgL8D/ucDAKCMstvtqlixosLCwnTvvffq8OHDOn78uBwOh/bs2SPpbOh2OByF3veiRYt05513atOmTZKklJQUzZo1S9WqVVNISIj3eUFBQTp06JCCgoKUkJCgW265JcdX9rXeAAD8HXF6OQAAfwFr167V5ZdfrksvvVQNGzbU3r17lZKSoszMTFWtWrXQ+5s1a5beffddPfnkk3ruuef0888/q3r16nrqqad8npcd6O12uyzLktvt1oYNG7zr7733XmVlZV3YNwcAQBlG6AYAoIzavXu3mjVrpqysLLndbs2fP18rVqxQZGSktmzZor1796pevXrat29fofdts9nUu3dv1a9fXwMGDNCpU6e0evXqfE8Pt9lshVoOAMDfAaEbAIAyqnbt2po3b57S09P17rvvauTIkWrQoIHq1q2r06dP6z//+U+RQ3e2zMxMZWZmqkqVKpowYYJmzJihcuXK5fn8o0eP6pZbbvE+PnLkSJGPDQDAXwHXdAMAUEYFBAQoIiJCderU0ejRo3X8+HFt3LhRknT11Vfr008/VWRkZJH3/8MPP2jw4MGaMmWKli1bpoMHD+rRRx9VRkZGntuEh4frs88+8341aNCgyMcHAOCvgNANAMBfRPY11ZIUGRmpzZs3q379+oXej8vl0qxZs/Tkk0/qH//4hzp06KDq1atr4cKFOnr0qMaOHZvrdh6PJ8+6AAD4u+L0cgAAyiiPx6NTp04pNTVV7733njIzM9W8eXNJZ0N3QECArrrqqkLv94svvlBcXJzmz5+vxo0be5eHh4dr9uzZ+V677XA4fE4vlySnk7cbAIC/L/4XBACgjNq9e7eaN2+uwMBA1a1bVzNmzNC6deskSfXr11ft2rUVGBjo9/6yR6o7duyoW265JddJ084fOU9PT5ckZWVlqU6dOvrss89y3fepU6fkcrl8bjcGAMDfgc3inC8AACCpffv2ysjIUFBQUL7PsyxLV1xxhRYvXqy9e/eqS5cu+vHHH1W+fPlcn79+/XoNGjRILVu21Ny5c02UDgBAqUXoBgAAheLxeJSVleX3KLrL5dKZM2d06aWXmi0MAIBSiNANAAAAAIAhzF4OAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAM+X/qCFBqG3lSCwAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 75
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:29:23.020554Z",
     "start_time": "2025-09-06T16:29:23.012483Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from scipy.stats import kruskal\n",
    "import numpy as np\n",
    "# 按组提取数据\n",
    "groups = [processed_df[processed_df['group'] == i]['最早达到目标Y染色体浓度的孕周'].values for i in range(5)]\n",
    "\n",
    "# 执行Kruskal-Wallis H检验\n",
    "stat, p_value = kruskal(*groups)\n",
    "\n",
    "print(f\"Kruskal-Wallis H 检验: H = {stat:.4f}, p = {p_value:.4f}\")\n",
    "if p_value<=0.05:\n",
    "    print('不同BMI组的最早达标时间存在显著差异 → 推荐分组策略合理。')\n",
    "else:\n",
    "    print('分组无统计学意义 → 需重新审视分组逻辑。')"
   ],
   "id": "dd81e445f6b997ad",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kruskal-Wallis H 检验: H = 10.3687, p = 0.0347\n",
      "不同BMI组的最早达标时间存在显著差异 → 推荐分组策略合理。\n"
     ]
    }
   ],
   "execution_count": 77
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:43:21.010794Z",
     "start_time": "2025-09-06T16:43:20.894196Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# DCA决策分析\n",
    "# 1. 数据预处理\n",
    "processed_df_ratio['达标'] = (processed_df_ratio['推荐检测时间(周)'] >= processed_df_ratio['最早达到目标Y染色体浓度的孕周']).astype(int)\n",
    "y_true = processed_df_ratio['达标'].values\n",
    "N = len(y_true)\n",
    "\n",
    "# 2. 定义计算净获益的函数\n",
    "def calculate_net_benefit(y_true, y_pred, theta):\n",
    "    TP = np.sum((y_pred >= theta) & (y_true == 1))\n",
    "    FP = np.sum((y_pred >= theta) & (y_true == 0))\n",
    "    P = np.sum(y_true == 1)\n",
    "    N_total = len(y_true)\n",
    "    net_benefit = (TP - (FP * theta / (1 - theta))) / (N_total - P)\n",
    "    return net_benefit\n",
    "\n",
    "# 3. 生成阈值范围\n",
    "thresholds = np.linspace(0.01, 1, 100)  # 避免除以0，从0.01开始\n",
    "\n",
    "# 4. 计算推荐策略的净获益\n",
    "net_benefits = []\n",
    "for theta in thresholds:\n",
    "    nb = calculate_net_benefit(y_true, y_true, theta)  # y_pred = y_true（推荐策略）\n",
    "    net_benefits.append(nb)\n",
    "\n",
    "# 5. 计算基线策略的净获益\n",
    "# 基线1：所有人都不检测（净获益 = 0）\n",
    "baseline_0 = [0] * len(thresholds)\n",
    "\n",
    "# 基线2：所有人都检测（净获益 = (TP - FP * theta) / (N - P)）\n",
    "net_benefits_all = []\n",
    "for theta in thresholds:\n",
    "    TP_all = np.sum(y_true == 1)  # 所有预测为1时，TP = 真实正例数\n",
    "    FP_all = np.sum(y_true == 0)  # 所有预测为1时，FP = 真实负例数\n",
    "    nb_all = (TP_all - (FP_all * theta / (1 - theta))) / (N - np.sum(y_true == 1))\n",
    "    net_benefits_all.append(nb_all)\n",
    "\n",
    "# 6. 绘制DCA曲线\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(thresholds, net_benefits, label='推荐策略', color='blue')\n",
    "plt.plot(thresholds, baseline_0, 'k--', label='不检测')  # 基线1\n",
    "plt.plot(thresholds, net_benefits_all, 'r--', label='统一检测')  # 基线2\n",
    "plt.xlabel('阈值概率')\n",
    "plt.ylabel('净获益')\n",
    "plt.title('决策曲线分析（DCA）')\n",
    "plt.ylim(-20, 5)\n",
    "plt.legend()\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ],
   "id": "35d30caddefd1e6d",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\20900\\AppData\\Local\\Temp\\ipykernel_34504\\1347289288.py:13: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  net_benefit = (TP - (FP * theta / (1 - theta))) / (N_total - P)\n",
      "C:\\Users\\20900\\AppData\\Local\\Temp\\ipykernel_34504\\1347289288.py:34: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  nb_all = (TP_all - (FP_all * theta / (1 - theta))) / (N - np.sum(y_true == 1))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 82
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:51.925003Z",
     "start_time": "2025-09-06T16:04:51.633139Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 在绘制一些图\n",
    "fig = plt.figure(figsize=(16, 7))\n",
    "# 各BMI组最早达标时间的小提琴图/箱线图\n",
    "# 用来评估BMI分组的合理性\n",
    "import seaborn as sns\n",
    "fig.add_subplot(121)\n",
    "sns.boxplot(data=processed_df,x = 'group',y=processed_df['最早达到目标Y染色体浓度的孕周'])\n",
    "plt.xlabel('BMI分组')\n",
    "plt.ylabel('最早达到目标Y染色体浓度的孕周')\n",
    "plt.title('各BMI组的Y染色体最早达标时间分布箱线图')\n",
    "\n",
    "fig.add_subplot(122)\n",
    "bmi_median = processed_df.groupby('group')['孕妇BMI'].median()\n",
    "# 推荐检测时间 vs. BMI 散点 + 分段折线图\n",
    "plt.step(bmi_median, result_df['推荐检测时间(周)'])\n",
    "plt.scatter(bmi_median, result_df['推荐检测时间(周)'], c='red')\n",
    "plt.xlabel(\"BMI\")\n",
    "plt.ylabel(\"推荐NIPT检测时间 (周)\")\n",
    "plt.title(\"BMI与最佳检测时点关系\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "8dabf87911e1ff4d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x700 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 63
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:53.686760Z",
     "start_time": "2025-09-06T16:04:51.961608Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# -------------------------------\n",
    "# Step: 蒙特卡洛误差敏感性分析（100次模拟）\n",
    "n_simulations = 100\n",
    "np.random.seed(42)\n",
    "\n",
    "# 存储每次模拟中每组的推荐时间（按组在 group_summary 中的顺序）\n",
    "t_recs = [[] for _ in range(optimal_k)]  # 列表的列表，索引对应排序后的组\n",
    "bmi_mean_per_group = [[] for _ in range(optimal_k)]\n",
    "t_star_median_per_group = [[] for _ in range(optimal_k)]\n",
    "\n",
    "# 提取原始特征\n",
    "X_bmi = processed_df['孕妇BMI'].values\n",
    "X_tstar = processed_df['最早达到目标Y染色体浓度的孕周'].values\n",
    "X = np.column_stack([X_bmi, X_tstar])\n",
    "\n",
    "print(f\"📊 开始蒙特卡洛误差敏感性分析（{n_simulations} 次模拟）...\")\n",
    "print(f\"   使用最优聚类数 k = {optimal_k}，每次模拟对 (BMI, t*) 添加噪声\")\n",
    "\n",
    "for sim in range(n_simulations):\n",
    "    # 添加噪声\n",
    "    noise_bmi = np.random.normal(0, 0.01, X_bmi.shape)      # BMI 测量误差\n",
    "    noise_tstar = np.random.normal(0, 0.2, X_tstar.shape)  # t* 检测误差\n",
    "\n",
    "    X_noisy_bmi = np.clip(X_bmi + noise_bmi, 20.0, 46.0)\n",
    "    X_noisy_tstar = np.clip(X_tstar + noise_tstar, 10.0, 25.0)\n",
    "    X_noisy = np.column_stack([X_noisy_bmi])\n",
    "\n",
    "    # 标准化\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X_noisy)\n",
    "\n",
    "    # 聚类\n",
    "    kmeans_sim = KMeans(n_clusters=optimal_k, random_state=42, n_init=10)\n",
    "    labels_sim = kmeans_sim.fit_predict(X_scaled)\n",
    "\n",
    "    # 构造加噪后的 DataFrame\n",
    "    df_sim = processed_df.copy()\n",
    "    df_sim['孕妇BMI'] = X_noisy_bmi\n",
    "    df_sim['最早达到目标Y染色体浓度的孕周'] = X_noisy_tstar\n",
    "    df_sim['group'] = labels_sim\n",
    "\n",
    "    # 按组统计并排序（按 BMI 均值）\n",
    "    group_summary_sim = df_sim.groupby('group').agg(\n",
    "        bmi_mean=('孕妇BMI', 'mean'),\n",
    "        bmi_min=('孕妇BMI', 'min'),\n",
    "        bmi_max=('孕妇BMI', 'max'),\n",
    "        count=('孕妇BMI', 'size'),\n",
    "        t_star_median=('最早达到目标Y染色体浓度的孕周', 'median')\n",
    "    ).sort_values('bmi_mean')  # 关键：必须排序以保证组顺序一致\n",
    "\n",
    "    # 生成不重叠 BMI 区间（左闭右开）\n",
    "    bmi_intervals_sim = []\n",
    "    prev_max = 18.5\n",
    "    for idx, row in group_summary_sim.iterrows():\n",
    "        low = max(prev_max, row['bmi_min'] - 0.1)\n",
    "        high = row['bmi_max'] + 0.1\n",
    "        bmi_intervals_sim.append((low, high))\n",
    "        prev_max = high\n",
    "\n",
    "    # 给每组分配区间（用于后续匹配）\n",
    "    group_summary_sim['interval_low'] = [i[0] for i in bmi_intervals_sim]\n",
    "    group_summary_sim['interval_high'] = [i[1] for i in bmi_intervals_sim]\n",
    "\n",
    "    # 对排序后的每组计算推荐时间\n",
    "    for idx, (group_id, row) in enumerate(group_summary_sim.iterrows()):\n",
    "        group_mask = (df_sim['group'] == group_id)\n",
    "        group_tstars = df_sim.loc[group_mask, '最早达到目标Y染色体浓度的孕周'].values\n",
    "\n",
    "        if len(group_tstars) == 0:\n",
    "            t_recs[idx].append(np.nan)\n",
    "            bmi_mean_per_group[idx].append(np.nan)\n",
    "            t_star_median_per_group[idx].append(np.nan)\n",
    "            continue\n",
    "\n",
    "        # 定义风险函数\n",
    "        def risk_func(t):\n",
    "            total = 0\n",
    "            for t_star_i in group_tstars:\n",
    "                acc_risk = 10.0 if t < t_star_i else 0\n",
    "                delay_risk = 1.0 * max(0, t - 12)\n",
    "                total += acc_risk + delay_risk\n",
    "            return total / len(group_tstars)\n",
    "\n",
    "        # 优化最佳检测时间\n",
    "        res = minimize_scalar(risk_func, bounds=(8, 25), method='bounded')\n",
    "        best_tg = res.x\n",
    "\n",
    "        # 保存结果（按排序后的组顺序）\n",
    "        t_recs[idx].append(best_tg)\n",
    "        bmi_mean_per_group[idx].append(row['bmi_mean'])\n",
    "        t_star_median_per_group[idx].append(row['t_star_median'])\n",
    "\n",
    "# -------------------------------\n",
    "# Step: 汇总结果\n",
    "# -------------------------------\n",
    "\n",
    "print(f\"\\n✅ 蒙特卡洛误差分析完成（{n_simulations} 次模拟）\\n\")\n",
    "\n",
    "# 输出每组推荐时间波动\n",
    "print(\"🔁 各组推荐检测时间波动（均值 ± 标准差）：\")\n",
    "for i in range(optimal_k):\n",
    "    t_list = [t for t in t_recs[i] if not np.isnan(t)]\n",
    "    if len(t_list) > 0:\n",
    "        mean_t = np.mean(t_list)\n",
    "        std_t = np.std(t_list)\n",
    "        print(f\"  组 {i}: {mean_t:.2f} ± {std_t:.2f} 周\")\n",
    "    else:\n",
    "        print(f\"  组 {i}: 无有效数据\")\n",
    "\n",
    "# 输出每组特征稳定性\n",
    "print(\"\\n📊 每组特征稳定性（加噪后平均）：\")\n",
    "for i in range(optimal_k):\n",
    "    bmi_list = [x for x in bmi_mean_per_group[i] if not np.isnan(x)]\n",
    "    tstar_list = [x for x in t_star_median_per_group[i] if not np.isnan(x)]\n",
    "    if len(bmi_list) > 0:\n",
    "        avg_bmi = np.mean(bmi_list)\n",
    "        avg_tstar_med = np.mean(tstar_list)\n",
    "        print(f\"  组 {i}: 平均BMI={avg_bmi:.2f}, 平均中位达标时间={avg_tstar_med:.2f}周\")"
   ],
   "id": "573cfd4e3becbac9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 开始蒙特卡洛误差敏感性分析（100 次模拟）...\n",
      "   使用最优聚类数 k = 5，每次模拟对 (BMI, t*) 添加噪声\n",
      "\n",
      "✅ 蒙特卡洛误差分析完成（100 次模拟）\n",
      "\n",
      "🔁 各组推荐检测时间波动（均值 ± 标准差）：\n",
      "  组 0: 13.88 ± 0.18 周\n",
      "  组 1: 13.86 ± 0.17 周\n",
      "  组 2: 14.05 ± 0.10 周\n",
      "  组 3: 13.85 ± 0.13 周\n",
      "  组 4: 16.52 ± 0.27 周\n",
      "\n",
      "📊 每组特征稳定性（加噪后平均）：\n",
      "  组 0: 平均BMI=28.68, 平均中位达标时间=12.76周\n",
      "  组 1: 平均BMI=30.71, 平均中位达标时间=12.70周\n",
      "  组 2: 平均BMI=33.09, 平均中位达标时间=12.81周\n",
      "  组 3: 平均BMI=35.67, 平均中位达标时间=13.25周\n",
      "  组 4: 平均BMI=41.06, 平均中位达标时间=16.16周\n"
     ]
    }
   ],
   "execution_count": 64
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:53.822391Z",
     "start_time": "2025-09-06T16:04:53.730431Z"
    }
   },
   "cell_type": "code",
   "source": [
    "box_data = [ [t for t in t_recs[g] if not np.isnan(t)] for g in range(optimal_k) ]\n",
    "plt.boxplot(box_data, labels=[f'组 {g}' for g in range(optimal_k)])\n",
    "plt.ylabel('推荐检测时间 (周)')\n",
    "plt.title(f'蒙特卡洛模拟下各组推荐时间分布（{n_simulations} 次）')\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()"
   ],
   "id": "e9f9685eefc391e7",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\20900\\AppData\\Local\\Temp\\ipykernel_34504\\2747681935.py:2: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
      "  plt.boxplot(box_data, labels=[f'组 {g}' for g in range(optimal_k)])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 65
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-06T16:04:54.006041Z",
     "start_time": "2025-09-06T16:04:53.841593Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "groups = ['28.7', '30.7', '33.1', '35.7', '41.1']\n",
    "bmi = [28.68, 30.71, 33.09, 35.67, 41.06]\n",
    "rec_times = [13.88, 13.86, 14.05, 13.85, 16.52]\n",
    "actual_times = [12.76, 12.70, 12.81, 13.25, 16.16]\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# 双轴图：左侧为孕周，右侧为 BMI\n",
    "ax1.plot(groups, rec_times, 'o-', label='推荐检测时间', color='darkblue', linewidth=2)\n",
    "ax1.plot(groups, actual_times, 's--', label='中位达标时间', color='red', linewidth=1.5)\n",
    "ax1.set_xlabel('组别（按 BMI 升序）')\n",
    "ax1.set_ylabel('孕周', color='black')\n",
    "ax1.tick_params(axis='y', labelcolor='black')\n",
    "ax1.legend(loc='upper left')\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# 添加 BMI 数值（右侧轴）\n",
    "ax2 = ax1.twinx()\n",
    "ax2.scatter(groups, bmi, color='gray', marker='x', s=60, label='平均 BMI')\n",
    "ax2.set_ylabel('BMI (kg/m²)', color='gray')\n",
    "ax2.tick_params(axis='y', labelcolor='gray')\n",
    "ax2.legend(loc='upper right')\n",
    "\n",
    "plt.title('BMI 分组与个性化 NIPT 检测时间推荐（蒙特卡洛平均）')\n",
    "plt.show()"
   ],
   "id": "a2f1afeb681aa81a",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\envs\\mm\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 178 (\\N{SUPERSCRIPT TWO}) missing from font(s) SimHei.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 66
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
